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Computer Vision Assignment Help

Codersarts  is top rated website for  Computer vision Projects, Assignments and Programming Homework Help site. Our dedicated team of Computer vision assignment expert will help and will guide you throughout your learning Computer vision journey.

 

Computer Vision programming is quite complicated, and there is nothing wrong or unusual to look for assignment help to deal with it. If you come to codersarts you will quickly find all the answers you need,Leaning Computer Vision projects is one of the top priorities of many students at the university. For Predicting business or statistical data, Computer Vision is one of the favorite options. You can expect a tough time while learning Computer Vision at the beginning. Assignments based on Computer Vision are quite intensive due to a large number of concepts.

 

Hence, you might find yourself in a situation where you need help with Computer Vision assignment. The programming part is always convoluted, and it keeps students puzzled. It is why codersarts.com has appointed the best programming experts to assist you with ML assignments. Our Computer Vision Assignment help tutors will ensure that your programming skills improve within a short span

Computer Vision Assignment topics

​How computers can gain high-level understanding from digital images or videos and take actions based on an understanding of the image. There are many types of computer vision that are used in different ways:

  • Image segmentation partitions an image into multiple regions or pieces to be examined separately.

  • Object detection identifies a specific object in an image. Advanced object detection recognizes many objects in a single image: a football field, an offensive player, a defensive player, a ball and so on. These models use an X,Y coordinate to create a bounding box and identify everything inside the box.

  • Facial recognition is an advanced type of object detection that not only recognizes a human face in an image, but identifies a specific individual.

  • Edge detection is a technique used to identify the outside edge of an object or landscape to better identify what is in the image.

  • Pattern detection is a process of recognizing repeated shapes, colors and other visual indicators in images.

  • Image classification groups images into different categories.

  • Feature matching is a type of pattern detection that matches similarities in images to help classify them.

Simple applications of computer vision may only use one of these techniques, but more advanced uses, like computer vision for self-driving cars, rely on multiple techniques to accomplish their goal.

Hire Dedicated Computer Vision Assignment Experts

At Codersarts.com we offer solutions to all type of Computer Vision- Data Preparation, Model design and Optimization, Application Development and System integration. Our team is equipped with all your ML needs. Computer Vision is one of the parts of AI serving the industry from a long time and known for fast areas of availability worldwide. It is the most demanding skill and choice for a developer for many good reasons, and the trend is expected to continue for many years to come and digitization, the need of Computer vision developer is also increasing day by day. 

 

Our team of computer vision developers develops custom image and video analysis software for machine vision and computer vision systems. We build computer vision software that can perform multiple tasks, including face analysis, real-time gesture and movement recognition, machine vision and image classification. 

Computer Vision Projects

Face detection Project Help

Face detection is one of the most fundamental aspects of computer vision. It is the base of many further studies like identifying specific people to marking key points on the face.  At CodersArts we provide assignment help on all sorts of facial detection systems using technologies like 

Haar Cascades: The haarcascade_frontalface_default. XML is a haar cascade designed by OpenCV to detect the frontal face. A Haar Cascade works by training the cascade on thousands of negative images with the positive image superimposed on it. The haar cascade is capable of detecting features from the source.

Dlib Frontal Face Detector: It's a landmark's facial detector with pre-trained models, the dlib is used to estimate the location of 68 coordinates (x, y) that map the facial points on a person's face.

DNN Face Detector in OpenCV: With the release of OpenCV 3.3 the deep neural network ( dnn. ) library has been substantially overhauled, allowing us to load pre-trained networks via the Caffe, TensorFlow, and Torch/PyTorch frameworks and then use them to classify input images and detect the contours in each frame where the face is located.

MTCNN (Multi-task Cascaded Convolutional Neural Networks): MTCNN is mostly used alongside the pre-trained facet model to embed the faces of all the participants in the image. This Joint Face Detection Alignment is able to simultaneously propose bounding boxes, five-point facial landmarks, and detection probabilities.

Dlib HOG based frontal face detector: Another attractive feature of Dlib is its HOG based models. These models are meant to be a good “frontal” face detector and it is, indeed. It detects faces even when they are not perfectly frontal to a good extent.

Tasks Associated with Face Detection:

  • Pose: The images of a face vary due to the relative camera-face pose (frontal, 45 degree, profile, upside down), and some facial features such as an eye or the nose may become partially or wholly occluded.
     

  • Presence or absence of structural components: Facial features such as beards, mustaches, and glasses may or may not be present and there is a great deal of variability among these components including shape, color, and size. 
     

  • Facial expression: The appearance of faces is directly affected by a person’s facial expression. 
     

  • Occlusion: Faces may be partially occluded by other objects. In an image with a group of people, some faces may partially occlude other faces. 
     

  • Image orientation: Face images directly vary for different rotations about the camera’s optical axis. 
     

  • Imaging conditions: When the image is formed, factors such as lighting (spectra, source distribution and intensity) and camera characteristics (sensor response, lenses) affect the appearance of a face.

 

  • Face Matching: Find the best match for a given face.

  • Face Similarity: Find faces that are most similar to a given face.

  • Face Transformation: Generate new faces that are similar to a given face.

TRANSFER LEARNING FOR IMAGE CLASSIFICATION  PROJECT HELP

 

(TL) is a research problem in machine learning (ML) that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem. For example, the knowledge gained while learning to recognize cars could apply when trying to recognize trucks. Through our traditional methods, the need for huge amounts of data is essential but this is not always the case sometimes we have only less amount of data at our hand. To deal with this lack of data, we make use of a technique called transfer learning. Transfer learning is a method that allows us to use the knowledge gained from other tasks in order to tackle new but similar problems quickly and effectively. This reduces the need for data related to the specific task we are dealing with. There are many pre-trained models available on the internet which could be used to extract features from the dataset in case the amount of data available is low.

Types of Transfer learning

  • Freeze Convolutional Base Model

  • Train selected top layers in the base model

  • Combination of steps a and b.

OPTIMIZATION TECHNIQUES FOR TL

  • Learning Rate

  • Model Architecture

  • Type of transfer learning

  • Optimisation technique

  • Regularisation

CHOICE OF PRE-TRAINED MODELs

  • vgg16-19

  • ResNet

  • InceptionV3,

  • ResNet,

  • MobileNet,

  • Xception,

  • InceptionResNetV2

  • Facenet

GENERALIZED STEPS INVOLVED TO PERFORM TL TASKS ON VISION DATA

  • Define a model

  • Find ideal initial learning rate

  • Create a module for scheduling the learning rate

  • Augment the Images

  • Apply the transformation(mean subtraction) for better fine-tuning

  • Test on a smaller set

  • Fit the model

  • Test the model on random images

  • Visualize the kernels to validate if the training has been successful.

Optical Character Recognition(OCR) Project Help

One well-known application of A.I. is Optical Character Recognition (OCR).OCR, is a technology that enables you to convert different types of documents, such as scanned paper documents, PDF files or images captured by a digital camera into editable and searchable data.  Tesseract does various image processing operations internally (using the Leptonica library) before doing the actual OCR.

OCR has been around for decades, and its most common use is to convert an image into searchable text. Obviously, the accuracy of the conversion is important, at Codersarts most OCRs provide 98 to 99 percent accuracy, measured at the page level.

A list of state of art ocr’s are presented in the list below:

 

  1. OmniPage Ultimate

  2. Abbyy FineReader

  3. Adobe Acrobat Pro DC

  4. Readiris

  5. Rossum

 

At Codersarts we also build custom OCR using frameworks like cv2 & pytesseract. There are numerous other public domain OCR libraries. However, the best omni-font OCR libraries are not public domain libraries. All modern OCR packages make use of Omni-font based recognition capability.

In python for example, use of the skimage functions like Regionprops, label, Clear_border, threshold_otsu, hog (Histogram of Gradients) to feed a Chars74k classifier.

Gesture recognition Project Help

Gesture recognition is a type of perceptual computing user interface that allows computers to capture and interpret human gestures as commands. The general definition of gesture recognition is the ability of a computer to understand gestures and execute commands based on those gestures. Gesture recognition based on the computer vision obtains the picture containing the human hand by cameras and then utilizes the image processing and machine learning to contribute to the judgment and recognition of the gesture comprehensively. At present, the steps of gesture recognition based on computer graphics are split into the following stages:

  • image collection

  • hand detection

  • segmentation

  • gesture recognition

  • classification

Types of algorithms involved in building gesture-based models.

  • Skeletal-based algorithms

  • Appearance-based models

  • Electromyography-based models

  • 3D model-based algorithms

 

 

The interaction between humans and robots constantly evolves and adopts different tools and software to increase the comfort of humans. The OpenCV library is not enough to start your project. This library provides you the software side, but you also need hardware components. As for  hardware category enters a developed platform able to run the OpenCV library, webcams, and 3D sensors such as Kinect 3D.

The OpenCV is a free and open-source library focused on real-time image processing.It can detect and recognize a large variety of objects, but in this scenario, our focus mostly is to apply techniques and methods to detect and recognize the gestures of a human hand.

Human Pose Estimation Project Help

Human Pose estimation is an important problem that has enjoyed the attention of the Computer Vision community for the past few decades. It is a crucial step towards understanding people in images and videos. 

Human Pose Estimation is defined as the problem of localization of human joints (also known as keypoints - elbows, wrists, etc) in images or videos. It is also defined as the search for a specific pose in space of all articulated poses.

Types of Pose Estimation Systems:

  • 2D Pose Estimation - Estimate a 2D pose (x,y) coordinates for each joint from an RGB image.

  • 3D Pose Estimation - Estimate a 3D pose (x,y,z) coordinates a RGB image.

Human Pose Estimation has some pretty cool applications and is heavily used in Action recognition, Animation, Gaming, etc. For example, a very popular Deep Learning app HumanCourt uses Pose Estimation to analyze Basketball player movements. Some of the most successful methodologies involved in Computer Vision-based Pose Estimation are listed below:

  • DeepPose

  • Efficient Object Localization Using Convolutional Networks

  • Convolutional Pose Machines

  • Human Pose Estimation with Iterative Error Feedback

  • Deep High-Resolution Representation Learning for Human Pose Estimation

  • Stacked Hourglass Network For Human Pose Estimation

 

Smart Traffic Light System Using Deep learning

Traffic congestion is a huge problem in almost every developing country as the people using private vehicles are increasing each day and the capacity of the road networks is still not up to the mark. Vehicular traffic problems are very common in urban areas as both private vehicles and other public transportation services are huge in number due to the dense population. This problem affects the functioning of the city. Every individual has to schedule his/her day within the 24 hours time limit.

One of the core components of a smart city is automated traffic management. And that got me thinking – could I use my data science chops to build a vehicle detection model that could play a part in smart traffic management?

Think about it – if you could integrate a vehicle detection system in a traffic light camera, you could easily track the number of useful things simultaneously:

  • How many vehicles are present at the traffic junction during the day?

  • What time does the traffic build-up?

  • What kind of vehicles is traversing the junction (heavy vehicles, cars, etc.)?

  • Is there a way to optimize the traffic and distribute it through a different street?

However, traffic volumes in urban areas kill the potential time of the individuals. Also, huge amounts of fuel are wasted due to the increasing waiting time, particularly at signal points. Additionally, many urban areas are facing severe air pollution issues. This has a very high impact on the health and well-being of society. To address this issue, we need a better and efficient infrastructure of the city and the proper management of road traffic.

 

Nowadays, artificial intelligence (AI) and Computer vision are playing an important role in solving many of the real-world problems. We may use these Machine Learning techniques to address road traffic management problems.

As the manual maintenance is difficult and not sufficient with the increasing number of vehicles on roads, automation of traffic signal management with ML may result in better traffic conditions in urban areas.

The main idea behind this is to divide the system into the following phases.

 

In the first phase, we classify the traffic signal junctions into one of the three different zones.

  • High-level

  • Medium-level

  • Low-level traffic zones

 

In phase second we use classification algorithms like Support Vector Machine, to classify the traffic into given zones.

In the third phase, we optimize the signal configuration of high-level traffic zones to bring them to either medium-level or low-level traffic zones. Models like this are mostly built using TensorFlow, sklearn along with a few other utility packages like skimage.

Facial Recognition Project Help

With the rapid increase of computational powers and accessibility of innovative sensing, analysis and rendering equipment and technologies, computers are becoming more and more intelligent. Many research projects and commercial products have demonstrated the capability of a computer to interact with humans in a natural way by looking at people through cameras, listening to people through microphones, understanding these inputs, and reacting to people in a friendly manner. One of the fundamental techniques that enable such natural Human–Computer Interaction (HCI) is face detection. Face detection is the step stone to all facial analysis algorithms, including

  • Face alignment

  • Face modelling

  • Face relighting

  • Face recognition

  • Face verification/authentication

  • Head pose tracking

  • Facial expression tracking/recognition

  • Gender/age recognition, and many more

There are four milestone systems on deep learning for face recognition that drove these innovations, they are:

  • DeepFace

  • the DeepID series of systems

  • VGGFace

  • FaceNet.

 

Deep Face

DeepFace is a deep learning facial recognition system created by a research group at Facebook. It identifies human faces in digital images. It employs a nine-layer neural network with over 120 million connection weights and was trained on four million images uploaded by Facebook users

 

DeepID

First coined by Yi Sun in his paper Deep Learning Face Representation from predicting 10,000 classes, deep hidden identity for generic object detection, counted among the first models of deep learning for facial recognition. IT achieved more accuracy than humans on a project.

 

FaceNet

Achieving the state-of-the-art results on standard data sets, It uses a triplet loss function to learn score vectors for better results in feature extraction and, thus, identity verification. The paper contributed to understanding the development of a very large data set necessary to train modern CNN based face recognition systems. The data set acquired is then used as the basis for deep CNNs development for facial recognition tasks.

Image Segmentation Project Help

In digital image processing and computer vision, image segmentation is the process of partitioning a digital image into multiple segments (sets of pixels, also known as image objects).  Image segmentation is typically used to locate objects and boundaries in images.Each partition of the nodes (pixels) output from these algorithms are considered an object segment in the image. Some popular algorithms of this category are normalized cuts

  • Random walker

  • Minimum cut 

  • Isoperimetric partitioning

  • Minimum spanning tree-based segmentation

  • Segmentation-based object categorization.

These techniques separate the objects into different regions based on some threshold value. Not suitable when there are too many edges in the image and if there is less contrast between objects. Segmentation based on Clustering. Divides the pixels of the image into homogeneous clusters.Instead of predicting a single probability distribution for the whole image, the image is divided into a number of blocks and each block is assigned its own probability distribution.  Image segmentation typically generates a label image the same size as the input whose pixels are color-coded according to their classes.

Why Computer Vision is  important:

Computer vision is a part of artificial intelligence where computers can "see" the world, analyze visual data and then make decisions from it or gain understanding about the environment and situation. One of the driving factors behind the growth of computer vision is the amount of data we generate today that is then used to train and make computer vision better. Our world has countless images and videos from the built-in cameras of our mobile devices alone. But while images can include photos and videos, it can also mean data from thermal or infrared sensors and other sources.

Basic skill sets expected when you hire Machine Learning expert:

  • Knowledge of OOPs: Great Machine learning developers should be good in the implementation of object-oriented design patterns.

  • Knowledge of the Core Python: Before start machine learning first necessary to need to basic module, control flow and exception, import, and creating packages.

  • Knowledge of the basic algorithm: Have a some knowledge of ML algorithm before start ML.

  • Knowledge of Data Science: You have some knowledges of data science and its libraries.

  • Basic knowledge of statistical data: In machine learning, statistical calculation use operate any data it is necessary to have some basic knowledge of statistics.

Machine Learning libraries and tools required for ML Experts:

  • Machine Learning Support technologies: NLP, OpenCV, Artificial neural networks, Support vector machines

  • Machine Learning tools: Setup-tools, pip, etc

  • Test frameworks: UnitTest, py.test,etc

  • Asyncio: Python 3.5

  • Data analysis tools: NumPy, SciPy, SciPy, Matplotlib, Pandas, Scikit-learn (sklearn)

Let's see how Machine Learning is still relevant:

ML is solutions for all whether you are student, small or Medium level enterprises. You can see ML everywhere, ML development happen in the finance and insurance domain, industries, and healthcare.

Here are some of the common usage of ML in the real world:

  • Image recognition

  • Speech recognition

  • Recommendation systems

  • Medical Diagnosis

  • Statistical Calculations

  • Classification

  • Prediction

  • Extraction

  • Regression

  • Robotics - ROS

Hire Machine Learning Expert Online:

 Hire good ML Experts sometimes might be challenging for IT recruiter because it takes a long period of recruitment process and research to find a good one. You can search through a candidate’s LinkedIn profile or a resume all you want. If you can’t tell your JPA from your gradel you won’t be able to tell if the candidate is a good fit for the position you want to fill.There are lots of virtual assistants available,  you can outsource for your ML project to a reliable recruitment agency that offers experienced and qualified ML programmers to execute your project. Codersarts provides several hiring models and recruitment services, select the ideal fit and also can customized solutions to companies to fulfill their need for good and experienced ML Experts.

We offer:

  • Fast and reliable recruitment services

  • Staffing services in all parts of the world including US, UK, Middle East, etc

  • Recruitment services across more than 13 industries

  • Flexible hiring models to choose from

  • Thousands of ML projects successfully executed

  • A dedicated developer just for your project

  • Cost-efficient hiring models to suit your budget

  • Hire as per your project need, interview the candidate and select only when you are sure that he will meet your expectations

  • Transparent hiring model where we offer profiles of all candidates to you to shortlist candidates of your choice

  • Monitor performance of your candidate and keep a check on his performance as you do for your on role candidate

Codersarts offers  Machine Learning services for creating the right business pathway. We have ML Experts who possess the capabilities to offer out of the box,  ML development services by using the right tools and technologies.

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