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This paper proposes a real-time solution to setting up a virtual trial-room for on-line portals selling apparels using a generic web camera interface to the portal. The user selects an image of an apparel from the on-line display and captures his/her own videos. The proposed method detects the pose of the user as well as various anthropomorphic features such as length and thickness of upper limbs and the dimensions of the torso. We use a background subtraction based methodology to segment out the human body from the image. The segmented human body contour is represented by a 1D curve by computing the distance of a point on the contour from the body centroid. Various extremities of body parts are found out by measuring the curvature. Using the detected feature points, we use a cloth fitting algorithm to fit the garment to the users body. The entire process is performed at 30fps, providing a realistic rendering of virtual clothing for any user

Today low power implementation in the modern system on chips requires a holistic and concurrent approach which includes collaboration between power modeling and soft ware hardware co-design. Power gating is one of the emerging low power design techniques used in all the portable devices.The main goal of power gating is to eliminate the leakage current in standby mode. When the functional unit is powered on, a large and sudden inrush current is prompted through a low resistance path to ground. If' this current is excessive, then the produced surge may cause IR voltage drops and electromigration. This has a negative impact on circuit reliability and performance. Therefore an estimation of this maximum current during power up is essential for designing reliable and high performance CMOS combinational circuits. This paper describes important considerations of the high-level synthesis technique on the maximum power on inrush current. Based on this perception, a satisfiability (SAT) based approach is proposed in this paper. The problem of scheduling and functional unit binding is formulated as a satisfiability problem (SAT) and a PB SAT solver is utilized for discovering the optimal binding solution that minimizes the inrush current at the high level synthesis stage itself in the circuit design process.

Are existing object detection methods adequate for detecting text and visual elements in scientific plots which are arguably different than the objects found in natural images? To answer this question, we train and compare the accuracy of Fast/Faster R-CNN, SSD, YOLO and RetinaNet on the PlotQA dataset with over 220,000 scientific plots. At the standard IOU setting of 0.5, most networks perform well with mAP scores greater than 80% in detecting the relatively simple objects in plots. However, the performance drops drastically when evaluated at a stricter IOU of 0.9 with the best model giving a mAP of 35.70%. Note that such a stricter evaluation is essential when dealing with scientific plots where even minor localisation errors can lead to large errors in downstream numerical inferences. Given this poor performance, we propose minor modifications to existing models by combining ideas from different object detection networks. While this significantly improves the performance, there are still two main issues: (i) performance on text objects which are essential for reasoning is very poor, and (ii) inference time is unacceptably large considering the simplicity of plots. To solve this open problem, we make a series of contributions: (a) an efficient region proposal method based on Laplacian edge detectors, (b) a feature representation of region proposals that includes neighbouring information, (c) a linking component to join multiple region proposals for detecting longer textual objects, and (d) a custom loss function that combines a smooth L1-loss with an IOU-based loss. Combining these ideas, our final model is very accurate at extreme IOU values achieving a mAP of 93.44%@0.9 IOU. Simultaneously, our model is very efficient with an inference time 16x lesser than the current models, including one-stage detectors. Our model also achieves a high accuracy on an extrinsic plot-to-table conversion task with an F1 score of 0.77. With these contributions, we make a definitive progress in object detection for plots and enable further exploration on automated reasoning of plots.