Recently, medical image fusion has emerged as an impressive technique in merging the medical images of different modalities. Certainly, the fused image assists the physician in disease diagnosis for effective treatment planning. The fusion process combines multi-modal images to incur a single image with excellent quality, retaining the information of original images. This paper proposes a multi-modal medical image fusion through a weighted blending of high-frequency subbands of nonsubsampled shearlet transform (NSST) domain via chaotic grey wolf optimization algorithm. As an initial step, the NSST is applied on source images to decompose into the multi-scale and multi-directional components. The low-frequency bands are fused based on a simple max rule to sustain the energy of an individual. The texture details of input images are preserved by an adaptively weighted combination of high-frequency images using a recent chaotic grey wolf optimization algorithm to minimize the distance between the fused image and source images. The entire process emphasizes on retaining the energy of the low-frequency band and the transferring of texture features from source images to the fused image. Finally, the fused image is formed using inverse NSST of merged low and high-frequency bands. The experiments are carried out on eight different disease datasets obtained from Brain Atlas, which consists of MR-T1 and MR-T2, MR and SPECT, MR and PET, and MR and CT. The effectiveness of the proposed method is validated using more than 100 pairs of images based on the subjective and objective quality assessment. The experimental results confirm that the proposed method performs better in contrast with the current state-of-the-art image fusion techniques in terms of entropy, VIFF, and FMI. Hence, the proposed method will be helpful for disease diagnosis, medical treatment planning, and surgical procedure.

Artificial intelligence and computer vision have widespread applications in workout analysis. It has been extensively used in sports and the athlete industry to identify errors and improve performance. Furthermore, these methods prevent injuries caused by a lack of instructors or costly infrastructure. One such exercise is the squat, which is a movement in which a standing person descends to a posture with their torso vertical and their knees firmly bent, then returns to their original upright position. Each person’s squat is distinct, with varying limb lengths causing their form to change when observed. It has been observed that the mobility of various joints and muscular strength have a role in this. A squat improves the user by increasing overall leg strength, strengthening knee and hip joints, and lowering the risk of heart disease due to cardiovascular development. This paper presents a method for classifying squat types and recommending the right squat version. This study uses MediaPipe and a deep learning-based technique to decide if squatting is good or bad. A stacked Bidirectional Gated Recurrent Unit (Bi-GRU) model with an attention layer is proposed to consistently and fairly assess each user, categorizing squats into seven classes. This stacked Bi-GRU model with an attention unit is then compared to other cutting-edge models, both with and without the attention layer. The model outperforms other models by attaining an accuracy of 94% and is demonstrated to work the best and most consistently for our dataset. Furthermore, the individual executing the incorrect squat is corrected to the best of their ability, depending on their performance and body proportions, by providing the correct form.

The squat is a widely performed exercise essential for maintaining physical fitness and overall well-being. It involves a series of movements in which an individual lowers their torso by bending the knees, and then returns to the starting position. Incorrect squat execution can lead to physical injuries, highlighting the importance of expert guidance. However, many individuals cannot afford professional training due to high costs, necessitating an automated workout analysis system. Existing vision-based squat analysis models face challenges related to occlusion and environmental factors. To address these limitations, we propose a method that utilizes squat videos captured by a stereo camera. The MediaPipe algorithm extracts and tracks keypoints from video frames. Detected keypoints are then used to classify squat types and evaluate squat performance using a Bi-CGRU model. Additionally, we introduce a regression-based approach to predict the correct squat posture. A deep learning-based regressor, trained on a dataset of squat videos, estimates the correct joint angles. When an improper squat is detected, the system provides feedback by presenting the correct squat form to the participant. Experimental results demonstrate that the Bi-CGRU classification model achieves 96.1% accuracy, while the pose correction regression model attains 99.0% accuracy using mean squared error loss. Both qualitative and quantitative evaluations indicate that our approach outperforms state-of-the-art methods utilizing stereo video input. The system helps users to refine their squat technique and reduce the risk of injury by offering real-time posture correction based on biomechanics guidance.

Haze is a common atmospheric disturbance that adversely affects the quality of optical data, thus often restricting their usability. Since these effects are inherent in the process of spaceborne Earth sensing, it is important to develop effective methods to remove them. This work proposes a novel method for de-hazing satellite imagery and outdoor camera images. It is developed by modifying the transmission map used in Dark Channel Prior (DCP) method. A Weighted Variance Guided Filter (WVGF) is introduced for enhancing the image quality, which included a two-stage image decomposition and fusion process. The method also optimally combines the radiance and transmission components along with an additional stage modelling a fusion-based transparency function. A final guided filter-based image refinement scheme is incorporated to improve the processed image quality. The optimal tuning of the image-dependent parameters at various stages is achieved using the newly proposed Adaptive Black Widow Optimization (ABWO) algorithm, which makes the proposed de-hazing scheme fully automatic. Qualitative and quantitative performance analyses, and the results are compared with other state-of-the-art methods. The experimental results reveal that the proposed method performs better as compared with others, independent of the haze density, without losing the natural look of the scene.

The correlation filters (CF) have been extensively used in object tracking due to its robustness and attractive computational speed. However, the CF are more sensitive to object deformation because they are trained using the spatial features. Besides, updating the filter template with slightly drifted or occluded samples increase the probability of tracking failure. In contrast, the median flow tracker is complementary to the correlation techniques and is fast, robust to occlusion and deformation, but sensitive to illumination variation. In this paper, we exploit the advantage of correlation and optical flow based trackers to achieve drift free tracking. Hence, we apply the CF-based tracker to track an object and switch to the modified median flow tracker during the drift conditions. The combined model is optimized to cope up with the fast appearance change and recover from drifting. We also propose an adaptive feature selection process to select the most discriminative feature/features among colour name and histogram of oriented gradient features based on object separation from the background in intensity and colour channels. The proposed tracker updates the filter template dynamically, depending on the appearance of an object using an adaptive learning rate to track the object irrespective of occlusion, motion blur, and deformation. The scale of object is estimated using Lucas-Kanade homography method. The experiments are carried out using challenging video sequences from a standard object tracking benchmark dataset and show the best performance among the state-of-the-art techniques.