Motion estimation has become one of the most important techniques used in realtime computer vision application. There are several algorithms to estimate object motions. One of the most widespread techniques consists of calculating the apparent velocity field observed between two successive images of the same scene, known as the optical flow. However, the high accuracy of dense optical flow estimation is costly in run time. In this context, we designed an accurate motion estimation system based on the calculation of the optical flow of a moving object using the Lucas–Kanade algorithm. Our approach was applied on a local treatment region implemented into Raspberry Pi 4, with several improvements. The efficiency of our accurate realtime implementation was demonstrated by the experimental results, showing better performance than with the conventional calculation.

Efficient solutions must be considered, in order to solve the problem of intensive computing of the image processing applications and to achieve high real-time performance. The graphics processing unit (GPU) is an effective and the most recent method used for accelerating extensive calculation algorithms to reduce the execution time by exploiting the power of parallel programming techniques and to obtain the highest performance. In this paper, we present a parallel GPU implementation of an edge detection algorithm with a Sobel operator using CUDA (Compute Unifies Architecture) environment. Furthermore, we analyze and prove the high performance of GPU implementation, by testing the algorithm on a standard central processing unit (CPU) to compare the computational efficiency of these systems. Our experimental results show that the effectiveness of the GPU implementation by its higher performances compared to sequential calculation.

Deep learning is continuously evolving and making significant advances in several applications. Nevertheless, it has a remarkable influence in the field of image processing. Recently, deep learning has hit the motion estimation area well. Optical flow estimation is a mature and ever-growing field of research. It can be considered a multidisciplinary field. However, it is not easy to get dataset suitable for deep learning of its models. While they have made fundamental contributions, However, it is unclear how to generate more data and generalize it to live scene videos. In this paper, we have tried off extensive analyses and categorized various deep learning-based optical flow estimation techniques. Lately hybrid methods have been very successful. Despite their high performance, even they have performed the state of the art of certain datasets, most bibliographic studies haven’t taken into account these methods. For this, we have added a comparative section of these hybrid algorithms to this study. While describing the set of datasets commonly used by the scientific community, we’ve identified the differences and the correspondences between deep methods and conventional methods. We hope that this extensive research will be a fundamental resource for researchers in the field of image processing and help them better understand and use methods using motion estimation.

This paper provides an HEVC (High-Efficiency Video Coding) video compression or transcoding using Hybrid algorithm. The hybrid compression technique uses DWT (Discrete Wavelet Transformation) transform as well as that use DCT (discrete cosine transformation) transform.In the existing paper video compression was tested using DCT and DWT. In this paper testing is performed using Hybrid DWT-DCT and there by the input coding is not overlapped by another 2-D blocks. In the proposed work PSNR (Peak Signal to Noise Ratio) value is improved when compared to the existing work.Also, hardware implementation of one frame/image real-time HEVC decoder using Hybrid algorithm results is shown by using a field-programmable gate array (FPGA). Hardware implementation become more appealing due to their superior performance in terms of FPGA latency compared to CPU (Central Processing Unit) latency to execute or implement this Hybrid algorithm. Also, pipelined hybrid decoder architecture is used to absorb variations in processing time. This architecture achieves a target operating frequency of 150 MHz.

Motion estimation is considered among the most famous and important applications in the field of computer vision. Indeed, in the last decade, the optical flow has become an alternative technique to estimate motion on successive frames. However, dense and precise estimates of the optical flow are usually costly in computing time. In this context, our approach is focused upon calculating the moving object's optical flow through the use of the Lucas-Kanade algorithm in real time which is implemented into Raspberry Pi 4. The efficiency of our real time implementation is demonstrated by the experimental results, hence showing better performances in comparison with the conventional calculation.

Upper limb impairment is a common challenge for individuals who have suffered a stroke, particularly affecting distal joint mobility. In recent years, advancements in mobility aids, such as exoskeletons, have shown promise in improving motor skills for those with compromised mobility. In this study, we present the design and implementation of an upper limb exoskeleton capable of both Active Range of Motion (AROM) and Passive Range of Motion (PROM) modes. Additionally, the exoskeleton integrates automatic threshold detection for EMG signals, allowing for seamless interaction and assistance based on user muscle activation. By combining AROM and PROM modes with EMG signal threshold detection, our exoskeleton offers a versatile and responsive solution for individuals seeking to improve upper limb mobility following stroke or other health-related challenges.