Mk Bashar

Mk Bashar, Kuljit Bhatti, Gary Rohrer et al.

Sow body condition is an important indicator for growers as it has a large impact on lactation performance and piglet survival. However, body condition measures used during production, such as visual scoring and calipers, correlate poorly with underlying tissue composition. Ultrasound scans can provide direct measurements of subcutaneous backfat thickness and loin muscle depth, but their operation is labor intensive and not scalable for production. We present PigFormer, an end-to-end two-stage system that takes raw depth frames from a ceiling-mounted RGB-D camera and predicts subcutaneous backfat thickness, loin muscle depth, and total tissue thickness at the last rib. Stage 1 is a geometric front-end that converts raw depth into a standardized height map via SAM3-to-MaskDINO segmentation distillation, ground-plane removal, and orientation normalization. Stage 2 is a Slice Attention Encoder that treats each height map as a sequence of cross-sectional slices and captures spatial relationships along the full dorsal surface. On a multi-site dataset of 319 sow and gilt instances from two facilities, PigFormer achieves 2.43 mm backfat MAE and 3.87 mm overall MAE. It outperforms strong single-stage ResNet-18 and ViT-small baselines. PigFormer offers a practical path toward continuous, automated, non-contact body condition monitoring in commercial swine production. Code is available at https://github.com/iambashar/Pigformer.

Mk Bashar, Samia Islam, Kashifa Kawaakib Hussain et al.

Multiple object tracking gained a lot of interest from researchers in recent years, and it has become one of the trending problems in computer vision, especially with the recent advancement of autonomous driving. MOT is one of the critical vision tasks for different issues like occlusion in crowded scenes, similar appearance, small object detection difficulty, ID switching, etc. To tackle these challenges, as researchers tried to utilize the attention mechanism of transformer, interrelation of tracklets with graph convolutional neural network, appearance similarity of objects in different frames with the siamese network, they also tried simple IOU matching based CNN network, motion prediction with LSTM. To take these scattered techniques under an umbrella, we have studied more than a hundred papers published over the last three years and have tried to extract the techniques that are more focused on by researchers in recent times to solve the problems of MOT. We have enlisted numerous applications, possibilities, and how MOT can be related to real life. Our review has tried to show the different perspectives of techniques that researchers used overtimes and give some future direction for the potential researchers. Moreover, we have included popular benchmark datasets and metrics in this review.

Mk Bashar, Ocean Monjur, Samia Islam et al.

In recent years, Convolutional Neural Networks (CNNs), MLP-mixers, and Vision Transformers have risen to prominence as leading neural architectures in image classification. Prior research has underscored the distinct advantages of each architecture, and there is growing evidence that combining modules from different architectures can boost performance. In this study, we build upon and improve previous work exploring the complementarity between different architectures. Instead of heuristically merging modules from various architectures through trial and error, we preserve the integrity of each architecture and combine them using ensemble techniques. By maintaining the distinctiveness of each architecture, we aim to explore their inherent complementarity more deeply and with implicit isolation. This approach provides a more systematic understanding of their individual strengths. In addition to uncovering insights into architectural complementarity, we showcase the effectiveness of even basic ensemble methods that combine models from diverse architectures. These methods outperform ensembles comprised of similar architectures. Our straightforward ensemble framework serves as a foundational strategy for blending complementary architectures, offering a solid starting point for further investigations into the unique strengths and synergies among different architectures and their ensembles in image classification. A direct outcome of this work is the creation of an ensemble of classification networks that surpasses the accuracy of the previous state-of-the-art single classification network on ImageNet, setting a new benchmark, all while requiring less overall latency.