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Reference Paper IEEE 2019An Efficient Hand Gesture Recognition System Based on Deep CNNPublished in: 2019 IEEE International Conference on Industrial Technology (ICIT)https://ieeexplore.ieee.org/document/8755038. In the encoding phase, we reduced the loss of feature information by reducing the downsampling factor, which reduced the difficulty of tiny thin vessels segmentation. Various methods are available for eye tracking, some of which use special contact lenses, whereas others focus on electrical potential measurements. We propose the implementation method of bacteria recognition system using Python programing and the Keras API with TensorFlow Machine Learning framework. The first convolutional layer of the CNN serves as the preprocessing module to efficiently obtain the tampering artifacts. A stored database of the subjects is manipulated using image processing techniques to accomplish this task. Box filter based background estimation is used to smoothen the rapid variations, due to the movement of vehicles. The presented article details our platform for movement monitoring and fall-detection of persons based on data acquired from a Microsoft Kinect v2 sensor. Our proposed system runs on smartphones, which allow the user to take a picture of the food and measure the amount of calorie intake automatically. The text recognition is performed by employing an Optical Character Recognition (OCR) function. On top of that, it comes with intuitive dashboards that make it convenient for the teams to manage models in production seamlessly. Finally, extensive experimental results show that their denoiser is effective for those images with a large number of interference pixels which may cause misjudgement. Reference Paper IEEE 2019Adaptive Multiple-pixel Wide Seam CarvingPublished in: 2019 National Conference on Communications (NCC)https://ieeexplore.ieee.org/document/8732245. Moreover, the watermarked images’/frames’ errors, compared to their floating point counterparts, are very small, while robustness to various attacks is high. Afterwards, to evaluate image inpainting quality of the proposed method, we use Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) metrics and present some visual results. Template Matching is selected as method. A smart car service brings in addition to other services, an application through which the customer can see the repairs of the vehicle using only the license plate number extracted from a loaded image. Deep Learning for image captioning comes to your rescue. As new advances are being made in this domain, it is helping ML and Deep Learning experts to design innovative and functional Deep Learning projects. Its role is to connect the shallow layer pedestrian features to the deep layer pedestrian features and link the high and low resolution pedestrian features. 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. Sharp curve lane detection is one of the challenges of visual environment perception technology for autonomous driving. Image Segmentation Techniques using Digital Image Processing, Machine Learning and Deep Learning Methods. It aims to design an open-source Artificial General Intelligence (AGI) framework that can accurately capture the spirit of the human brain’s architecture and dynamics. Reference Paper IEEE 2019 Fish Tracking and Counting using Image Processing Published in: 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM) https://ieeexplore.ieee.org/document/8666369. The paper describes a vision based platform for real-life indoor and outdoor object detection in order to guide visually impaired people. To train our neural networks we provide two types of examples: images collected from the Internet and realistic examples generated by imposing various suitcases and bags over the scene’s background. Person wearing helmet in ATM center is one of the anomalous activity. Iris segmentation plays an important role in the iris recognition system, and the accurate segmentation of iris can lay a good foundation for the follow-up work of iris recognition and can improve greatly the efficiency of iris recognition. Our algorithm uses Kinect to identify the top three joints that could give the best identification results and then uses them for gait recognition. For this reason, in this paper, we introduce computation optimizations of the implemented algorithm to keep the integer part of arithmetic operations at optimal size, and, hence, arithmetic units as small as possible. For this project, you will use an FMA (Free Music Archive) dataset. In this study, they utilise CNN with the multi-layer structure for the removal of salt and pepper noise, which contains padding, batch normalisation and rectified linear unit. Finally, we present a final network implementation on a Raspberry Pi 3B that demonstrates a detection speed of 1.63 frames per second and an average precision of 0.842. There are two major techniques available to detect hand motion or gesture such as vision and non-vision technique and convert the detected information into voice through raspberry pi. The technology is still very young – it is developing as we speak. This work also involved the process of collecting samples of banana with different level of ripeness, application development and evaluation to improve the accuracy of the developed applications classification results using image processing and data mining techniques. With these extensions, not only can the hidden information be kept secure, but the system can be used to hide even more than a single image. In this review paper, three aspects are considered: image fusion methods on spatial domain and transform domain methods, Image fusion rules on transform domain method and image fusion metrics. The text recognition is performed by employing an Optical Character Recognition (OCR) function. Detectron offers a high-quality and high-performance codebase for object detection research. Reference Paper IEEE 2019Sharp Curve Lane Detection for Autonomous DrivingPublished in: Computing in Science & Engineering ( Volume: 21 , Issue: 2 , March-April 1 2019 )https://ieeexplore.ieee.org/document/8542714. For their copyright protection and authentication, watermarking can be used. When estimating the point of gaze, indentifying the visual focus of a person within a scene is required. All rights reserved, Although a new technological advancement, the scope of Deep Learning is expanding exponentially. To resolve this problem, smart and auto attendance management system is being utilized. Reference Paper IEEE 2019Computer Vision based drowsiness detection for motorized vehicles with Web Push NotificationsPublished in: 2019 4th International Conference on Internet of Things: Smart Innovation and Usages (IoT-SIU)https://ieeexplore.ieee.org/document/8777652. This research has used 218 images as training set and the systems shows an accuracy of 100% in Meningioma and 87.5% in Glioma classifications and an average confidence level of 94.6% in segmentation of Meningioma tumors. Deep Learning Project Ideas. Moving vehicle detection based on background subtraction, with fixed morphological parameters, is a popular approach in AVS systems. The approach is comprised of two stages: (i) static object detection based on background subtraction and motion estimation and (ii) abandoned luggage recognition based on a cascade of convolutional neural networks (CNN). VGG-16 based CNN is used to extract the feature from the given image. Moving vehicles are then detected by analyzing the pixel wise variations between estimated background and input frames. “Build a deep learning model in a few minutes? In this work vehicles and pedestrians are considered objects of interest. Usually, for face recognition, scale-invariant feature transforms (SIFT) and speed ed up robust features (SURF) have been used by the research community. In this article, we have provided a system of recognizing gestures continuously with the Indian Sign Language (ISL), which both hands are used to make every gesture. This is the reason why an increasing number of companies across all domains are adopting chatbots in their customer support infrastructure. This work assures the achievement of the identified particular requirements of digital watermarking when applied to digital medical images and also provides robust controls within medical imaging pipelines to detect modifications that may be applied to medical images during viewing, storing and transmitting. Second, the sampled frames of each video clip are fed into a pre-trained CNN model to generate the corresponding convolutional feature maps (CFMs). System achieves state-of-the-art results in ISSIA-CNR Soccer Dataset and its feasibility has been tested on 4 camera prototype system. Your email address will not be published. Reference Paper IEEE 2019Published in: 2019 21st International Conference on Advanced Communication Technology (ICACT)https://ieeexplore.ieee.org/document/8701964. These are only a handful of the real-world applications of Deep Learning made so far. System comprises of flexible detector and classical particle tracking. Then, the estimation models for the robot position and the line landmark are derived as simple linear equations. In this study, it is aimed to strengthen the LSB technique which is one of the steganography methods by suggesting the use of mask which will provide the least change on the image while hiding the data into a digital image. To protect the copyright of digital videos, video copy detection has become a hot topic in the field of digital copyright protection. This project is about collecting images of various infected, good and seems to be infected plant leafs. The expansion potential of this system can be known in public places where deaf people are communicating with ordinary people to send messages. 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. The detection of helmet is obtained using Deep Learning Convolutional Neural Network (CNN) architecture such as VGGNET (Visual Geometry Group) and ALEXNET. To test the capabilities of a neural network of this massive size, the Google Brain team fed the network with random thumbnails of cat images sourced from 10 million YouTube videos. Pre-processing gestures are obtained using histogram (OH) with PCA to reduce the dimensions of the traits obtained after OH. Recent developments in video processing using machine learning have enabled images obtained from cameras to be analysed with high accuracy. The incoming image is firstly enhanced by employing Contrast Limited Adaptive Histogram Equalization (CLAHE). Reference Paper IEEE 2019 A Vision Module for Visually Impaired People by Using Raspberry PI Platform Published in: 2019 15th International Conference on Engineering of Modern Electric Systems (EMES) https://ieeexplore.ieee.org/document/8795205. The aim is to optimize the likelihood of the training data, thereby makes the training procedure manageable and stable. The proposed platform is programmed in the C# programming language for more efficient real-time analysis of the obtained spatial data and future modularity – allowing the integration of other data sources (e.g., thermal sensors, accelerometer data or electrocardiogram recordings) to create a sophisticated monitoring platform. We first train a supervised convolutional neural network (CNN) to learn the hierarchical features of deblocking operations with labeled patches from the training datasets. Initially 72000+ specimens were used from NumtaDB (85000+) dataset for training and 1700+ specimens were used as test dataset. It can automatically generate APIs to help your developers incorporate AI into their applications readily. In the test set, compared with the traditional YOLO V3, the improved algorithm detection accuracy increased by 2.44% with the same detection rate. While large high-quality image … Face recognition technology is a subset of Object Detection that focuses on observing the instance of semantic objects. Reference Paper IEEE 2019Glaucoma Detection Using Fundus Images of The EyePublished in: 2019 XXII Symposium on Image, Signal Processing and Artificial Vision (STSIVA)https://ieeexplore.ieee.org/document/8730250. The system functionality is verified with the help of an experimental setup. Did you know that we are the most documented generation in history of humanity. Reference Paper IEEE 2019A Fuzzy Expert System Design for Diagnosis of Skin DiseasesPublished in: 2019 2nd International Conference on Advancements in Computational Sciences (ICACS)https://ieeexplore.ieee.org/document/8689140. As for the test set, it will include 1000 images that are randomly chosen from each of the ten classes. Image classification is a pivotal application in the field of deep learning, and hence, you will gain knowledge on various deep learning concepts while working on this project. However, 12 Sigma’s AI algorithm system can reduce the diagnosis time, leading to a better rate of survival for lung cancer patients. After pre-processing, particulars extraction is done trailed by post processing stage lastly the details coordinating is finished. Once you finish with these simple projects, I suggest you go back, learn a few more concepts and then try the intermediate projects. Mobile application has been identified as the best platform for the expert system tool to reach as many users as possible. The proposed method is also tested using the robotics advancement through web publishing of sensorial and elaborated extensive datasets benchmark dataset. However, above all, crop disease is the crucial factor and causes 20-30% reduction of the productivity in case of its infection. I need a CNN based image segmentation model including the pre-processing code, the training code, test code and inference code. All you need is to have Python 2/3 in your machine, a Bluemix account, and of course, an active Internet connection! The performance of our proposed model also compared with other existing works and presented here. Reference Paper IEEE 2019Intelligent monitoring of indoor surveillance video based on deep learningPublished in: 2019 21st International Conference on Advanced Communication Technology (ICACT)https://ieeexplore.ieee.org/document/8701964. Considering these limitations, researchers have studied palmprint, touchless fingerprint, and finger-knuckle-print recognition using the built-in visible light camera. Reference Paper IEEE 2019A Deep Learning RCNN Approach for Vehicle Recognition in Traffic Surveillance SystemPublished in: 2019 International Conference on Communication and Signal Processing (ICCSP)https://ieeexplore.ieee.org/document/8698018. The student will benefit from learning about various camera systems through planning and executing scientific imaging experiments. “Olivia” is a Virtual Assistant developed specifically for homes, which can be integrated into any home to make it a Smart Home. The techniques used for the whole process of face recognition are machine learning based because of their high accuracy as compared with other techniques. Image Synthesis 10. The algorithm is trained using the training dataset and tested using the test dataset. Reference Paper IEEE 2016Food calorie measurement using deep learning neural networkPublished in: 2016 IEEE International Instrumentation and Measurement Technology Conference Proceedingshttps://ieeexplore.ieee.org/document/7520547, Reference Paper IEEE 2019Single Image Dehazing Using Dark Channel Fusion and Haze Density WeightPublished in: 2019 IEEE 9th International Conference on Electronics Information and Emergency Communication (ICEIEC)https://ieeexplore.ieee.org/document/8784493. As the name suggests, this project involves developing a digit recognition system that can classify digits based on the set tenets. Human unique finger impression is wealthy in detail called particulars, which can be utilized as recognizable proof imprints for unique fingerprint confirmation. The implementation results have confirmed that bacteria images from microscope are able to recognize the genus of bacterium. The functioning of DeepMimic is pretty simple. In this work, we propose a method where our proposed CNN model which recognizes numerals with high degree of accuracy beyond 96%, even in most challenging noisy conditions. These transformation range from simple image manipulations to sophisticated machine learning-based adversaries. Deep learning and edge computing are the emerging technologies, which are used for efficient processing of huge amount of data with distinct accuracy. In this paper, a multiple layer message security scheme is proposed, utilizing 3D images. PACS). The parameters are chosen to compare the different mini batch size and epoch in ALEXNET. An extension of a benchmark dataset Food-101 is also created to include sub-continental foods. Reference Paper IEEE 2019Recognition of Diabetic Retinopathy Based on Transfer LearningPublished in: 2019 IEEE 4th International Conference on Cloud Computing and Big Data Analysis (ICCCBDA)https://ieeexplore.ieee.org/document/8725801. Firstly, we use skin color detection and morphology to remove unnecessary background information from the image, and then use background subtraction to detect the ROI. Experimental results show that followed approach brings appealing results on semantic food segmentation and significantly advances on food and non-food segmentation. 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. The word steganography combines the Greek words steganos , meaning “covered, concealed, or protected,” and graphein meaning “writing”. Reference Paper IEEE 2019 Deep Learn Helmets-Enhancing Security at ATMs Published in: 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS) https://ieeexplore.ieee.org/document/8728493, Your email address will not be published. According to gesture Recognized, various tasks can be performed like turning on the fan or lights. Based on the YOLO V3 full-regression deep neural network architecture, this paper utilizes the advantage of Densenet in model parameters and technical cost to replace the backbone of the YOLO V3 network for feature extraction, thus forming the so-called YOLO-Densebackbone convolutional neural network. This paper proposes foreground segmentation algorithm powered by the convolutional neural network. This project isn’t a very challenging one. At the same time, the real-time semantic segmenter S extracts the object-level semantics St. Then, some specific rules are applied on Bt and St to generate the final detection Dt. The experimental results demonstrate that the designed faster R-CNN network and the FP reduction scheme are effective in the lung nodule detection and the FP reduction for MR images. In this work, the ripeness of the banana is classified into three different class of maturity; unripe, ripe and overripe systematically based on their key attributes value.

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