Binary classification of the obtained visual image data into defect and defect-free sets is one sub-task of these systems and is still often carried out either completely manually by an expert or by using pre-defined features as classifiers for automatic image post-processing. The main motive of our project is to detect stress in the IT professionals using vivid Machine learning and Image processing techniques. Blood cell image classification is an important part for medical diagnosis system. The final segmented retina vessels contain more noise with low classification accuracy. This is one of the excellent deep learning project ideas. Figure 3: Neural network data training approach Figure 4: Image processing using deep learning Implementation: An example using AlexNet If you’re new to deep learning, a quick and easy way to get … OpenCog also encompasses OpenCog Prime – an advanced architecture for robot and virtual embodied cognition that includes an assortment of interacting components to give birth to human-equivalent artificial general intelligence (AGI) as an emergent phenomenon of the system as a whole. So, without further ado, let’s jump straight into some deep learning project ideas that will strengthen your base and allow you to climb up the ladder. Save my name, email, and website in this browser for the next time I comment. Reference Paper IEEE 2019Hiding Images Within ImagesPublished in: IEEE Transactions on Pattern Analysis and Machine Intelligence ( Early Access )https://ieeexplore.ieee.org/document/8654686. We report improvements for disc segmentation in comparison with other works on the literature, a novel method to segment the cup by thresholding and a new measure between the size of the cup and the size of the disc. Additionally, we explore food and non-food segmentation by getting advantage of supervised learning. Reference Paper IEEE 2019 Image Deblocking Detection Based on a Convolutional Neural Network Published in: IEEE Access ( Volume: 7 ) https://ieeexplore.ieee.org/document/8649625. The image size of the ROI is then resized to 100×120 and then entered into the deep convolutional neural network (CNN), in order to identify multiple hand gestures. Iris, fingerprint, and three-dimensional face recognition technologies used in mobile devices face obstacles owing to price and size restrictions by additional cameras, lighting, and sensors. Reference Paper IEEE 2019Improved Background Subtraction-based Moving Vehicle Detection by Optimizing Morphological Operations using Machine LearningPublished in: 2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA)https://ieeexplore.ieee.org/document/8778263. Softmax Regression or Multinomial Logistic Regression is the ideal choice for this project. Reference Paper IEEE 2019 Smart Home With Virtual Assistant Using Raspberry Pi Published in: 2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence) https://ieeexplore.ieee.org/document/8776918. Since this technique is a generalization of logistic regression, it is apt for multi-class classification, assuming that all the classes are mutually exclusive). As the name suggests, this project involves developing a digit recognition system that can classify digits based on the set tenets. An expert system is capable of providing timely and correct diagnosis, that’s why building an expert system is a potential challenge. What we need to do is migrate the DR images to these models. A visual tracking system is designed to track and locate moving object(s) in … Then, we implemented K-means algorithm to find appropriate anchors for head and facial features. The primary intended use of the platform is to monitor elderly people living alone and, in case of fall detection, transmit relevant information to relatives or medical staff and/or perform specific actions (e.g., turn off kitchen appliances). Written in Python, this Deep Learning project is based on the Caffe2 deep learning framework. However, it triggers a decrease in productivity as no taking appropriate action and time. Reference Paper IEEE 2019A Study on Feature Extraction Methods Used to Estimate a Driver’s Level of DrowsinessPublished in: 2019 21st International Conference on Advanced Communication Technology (ICACT)https://ieeexplore.ieee.org/document/8701928. Experimental results demonstrated the effectiveness of the proposed scheme over the conventional EZW and other improved EZW schemes for both natural and medical image coding applications. In this paper, we proposed a method for extracting detailed features of the eyes, the mouth, and positions of the head using OpenCV and Dlib library in order to estimate a driver’s level of drowsiness. When estimating the point of gaze, indentifying the visual focus of a person within a scene is required. The experimental results shows that the classification accuracy of this method can reach at 0.60, which is better than the traditional direct training method and has better robustness and generalization. IBM Watson is Integrated with the Watson Studio to empower cross-functional teams to deploy, monitor, and optimize ML/Deep Learning models quickly and efficiently. Thereby, the amount of actual defects that are falsely classified as negative are minimized. These transformation range from simple image manipulations to sophisticated machine learning-based adversaries. The test results show that the improved model can effectively deal with situations that the helmet is stained, partially occluded, or there are many targets with a low image resolution. This allows them to be used in various technical vision systems and video analysis systems. The government of India has hence come up with a new strategy which requires a high-sensitivity microscopy based TB diagnosis mechanism [2]. It has been noticed that the technological development is growing, so it is considered that there is a need for development in this field too, and a smart car service is the best option for car services. A. stated that if we train a neural network using a voluminous and rich dataset, we could create a deep learning model that can hallucinate colours within a black and white photograph. The FCN-AlexNet of deep learning method was used to segment images, and accurate localization of thyroid nodules was achieved. Third, based on the generated CFMs, we extract the CNN features on the spatial and temporal domains of each video clip, i.e., the spatio-temporal CNN features. For implementing the system, we use MATLAB fuzzy logic toolbox. These findings are based on Computer Vision Challenge on Bengali HandWritten Digit Recognition (2018) competition submissions. As for the test set, it will include 1000 images that are randomly chosen from each of the ten classes. Our method employs different deep learning models for accurate food identification. Then, we extract the features for an image with the CNN on the basis of a patch by applying a patch-sized sliding-window to scan the whole image. This project will strengthen your knowledge of CNN and LSTM, and you will learn how to implement them in real-world applications as this. A notable feature of this system is that it can run on ordinary laptops due to the small size of the fused dataset, which accelerates the calculation of recognition rate. Reference Paper IEEE 2019 Optimization and Hardware Implementation of Image and Video Watermarking for Low-Cost Applications Published in: IEEE Transactions on Circuits and Systems I: Regular Papers ( Volume: 66 , Issue: 6 , June 2019 ) https://ieeexplore.ieee.org/document/8694927. The main contribution of this paper is the development of an expert system tool for evaluating the ripeness of banana fruit. In this image colourization project, you will be using Python and OpenCV DNN architecture (it is trained on ImageNet dataset). The extracted text is pronounced by using a suitable speech synthesizer. With the advance of deep learning, facial recognition technology has also advanced tremendously. Most of the dumb people are deaf also. A computer-aided diagnosis (CAD) system based on mammograms enables early breast cancer detection, diagnosis, and treatment. Object detection represents the most important component of Automated Vehicular Surveillance (AVS) systems. Optical tracking is a nonintrusive technique that uses a sequence of image frames of eyes that have been recorded using video-capturing devices. In this research, we focused finger vein identification system by using our own finger vein dataset, we trained it with transfer learning of AlexNet model and verified by test images. In this deep learning project, you will learn how to perform human face recognition in real-time. This project proposes a method for diabetic retinopathy recognition based on transfer learning. Reference Paper IEEE 2019A Method for Localizing the Eye Pupil for Point-of-Gaze EstimationPublished in: IEEE Potentials ( Volume: 38 , Issue: 1 , Jan.-Feb. 2019 )https://ieeexplore.ieee.org/document/8595416. The main implementation steps used in this type of system are face detection and recognizing the detected face.This paper proposes a model for implementing an automated attendance management system for students of a class by making use of face recognition technique, by using Eigenface values, Principle Component Analysis (PCA) and Convolutional Neural Network (CNN). As a major novelty, we describe a processing chain based on convolution neural networks (CNNs) that defines the regions-of-interest in the input data that should be privileged in an implicit way, i.e., without masking out any areas in the learning/test samples. Image Synthesis 10. (Part 1) Deeksha Aggarwal. The two core components of this visual tracking system are: This is one of the excellent deep learning project ideas for beginners. All the local histograms are clustered together, and the cluster centers are used as the encrypted visual words. To distribute probabilities in a more efficient way, the proposed approach is based on increasing the number of coefficients not to be encoded by the use of new symbols. It has an accuracy of 98.5% using 2500 variant images in a class. This project combines deep learning methods, using the state-of-the-art framework for instance segmentation, called Mask R-CNN, to train the fine-tuning network on our datasets, which can efficiently detect objects in a video image … IEEE Digital Image Processing Projects The technique of digital image processing is used to enhance the quality of an image by applying arithmetical operations. In this project, we explore this problem from a new perspective and propose a novel background subtraction framework with real-time semantic segmentation (RTSS). The task requires CNN network to extract features from given image and upsample the image to segment background and foreground. So the normal people’s voice can be converted into their sign language. In this project non-vision based technique will be used. Reference Paper IEEE 2019Deep Learning based Automated Billing CartPublished in: 2019 International Conference on Communication and Signal Processing (ICCSP)https://ieeexplore.ieee.org/document/8697995. The method is developed to be applicable in real time on a low-cost embedded system for indoor service robots. This method constitutes an essential place in image processing. In the second phase, an interpolation of nonuniformly spaced samples based on pixel gray correction is proposed to get the high resolution (HR) image. The paper describes a vision based platform for real-life indoor and outdoor object detection in order to guide visually impaired people. We use five convolutional layer, three max-pooling layer and a fully connected network with single hidden layer. You will create a deep learning model that uses neural networks to classify the genre of music automatically. First, download data from Kaggle’s official website, then perform data enhancement, include data amplification, flipping, folding, and contrast adjustment. In this project, the problem of facial expression is addressed, which contains two different stages: 1. Reference Paper (IEEE 2019)Heart Rate Variability-Based Driver Drowsiness Detection and Its Validation With EEGPublished in: IEEE Transactions on Biomedical Engineering ( Volume: 66 , Issue: 6 , June 2019 )https://ieeexplore.ieee.org/document/8520803, Automated recognition of human activities or actions has great significance as it incorporates wide-ranging applications, including surveillance, robotics, and personal health monitoring.This paper presents a viable multimodal feature-level fusion approach for robust human action recognition, which utilizes data from multiple sensors, including RGB camera, Reference Paper IEEE 2019Robust Human Activity Recognition Using Multimodal Feature-Level FusionPublished in: IEEE Access ( Volume: 7 )https://ieeexplore.ieee.org/document/8701429.