Recognition of human activities and expressions in video sequences using shape context descriptor

The recognition of objects and classes of objects is of importance in the field of computer vision due to its applicability in areas such as video surveillance, medical imaging and retrieval of images and videos from large databases on the Internet. Effective recognition of object classes is still a challenge in vision; hence, there is much interest to improve the rate of recognition in order to keep up with the rising demands of the fields where these techniques are being applied. This thesis investigates the recognition of activities and expressions in video sequences using a new descriptor called the spatiotemporal shape context. The shape context is a well-known algorithm that describes the shape of an object based upon the mutual distribution of points in the contour of the object; however, it falls short when the distinctive property of an object is not just its shape but also its movement across frames in a video sequence. Since actions and expressions tend to have a motion component that enhances the capability of distinguishing them, the shape based information from the shape context proves insufficient. This thesis proposes new 3D and 4D spatiotemporal shape context descriptors that incorporate into the original shape context changes in motion across frames. Results of classification of actions and expressions demonstrate that the spatiotemporal shape context is better than the original shape context at enhancing recognition of classes in the activity and expression domains

Obesity Detrimental to Women’s Health

Obesity is the detrimental to overall health and physical performance. Excess amount of body fat is linked to several diseases including type 2 diabetes mellitus, hypertension, hyperlipidemia, cardiovascular diseases and certain type of cancers, and they increase the morbidity and mortality. The mortality rate increases by 50% to 100% when the body mass index (BMI) is equal to or greater than 30Kg.m-2. Most of the women after 30‘s suffered from abdominal obesity or disproportion in hip and waist ratio. It appears to serve as platform for variety of clinical health problems, in addition to greater risk of serious illness. It poses other mechanical limitation that limit performance of daily activities. As individual ages, they may lose the ability to regulate energy intake based on physiologic cues, leading to overeating and weight gain. High caloric food with low in nutrients density and sedentary life style are two major causes of obesity. Several methods are used to determine a person‘s ideal body weight; however in many cases especially for athletes, ideal body weight may be unrealistic. Thus, it is better to focus on a healthy body weight rather than ideal body weight. Healthy body weight is different for each individual, athlete or non athlete, and is one that is relative to a person‘s overall health profile. Prevention of weight gain would likely to decrease chronic disease, improve quality of life and decrease health care cost. So, weight management is required by an every individual by increasing the physical activity every day with proper diet

DeepJoin: Learning a Joint Occupancy, Signed Distance, and Normal Field Function for Shape Repair

We introduce DeepJoin, an automated approach to generate high-resolution repairs for fractured shapes using deep neural networks. Existing approaches to perform automated shape repair operate exclusively on symmetric objects, require a complete proxy shape, or predict restoration shapes using low-resolution voxels which are too coarse for physical repair. We generate a high-resolution restoration shape by inferring a corresponding complete shape and a break surface from an input fractured shape. We present a novel implicit shape representation for fractured shape repair that combines the occupancy function, signed distance function, and normal field. We demonstrate repairs using our approach for synthetically fractured objects from ShapeNet, 3D scans from the Google Scanned Objects dataset, objects in the style of ancient Greek pottery from the QP Cultural Heritage dataset, and real fractured objects. We outperform three baseline approaches in terms of chamfer distance and normal consistency. Unlike existing approaches and restorations using subtraction, DeepJoin restorations do not exhibit surface artifacts and join closely to the fractured region of the fractured shape. Our code is available at: https://github.com/Terascale-All-sensing-Research-Studio/DeepJoin.Comment: To be published at SIGGRAPH Asia 2022 (Journal

Pix2Repair: Implicit Shape Restoration from Images

We present Pix2Repair, an automated shape repair approach that generates restoration shapes from images to repair fractured objects. Prior repair approaches require a high-resolution watertight 3D mesh of the fractured object as input. Input 3D meshes must be obtained using expensive 3D scanners, and scanned meshes require manual cleanup, limiting accessibility and scalability. Pix2Repair takes an image of the fractured object as input and automatically generates a 3D printable restoration shape. We contribute a novel shape function that deconstructs a latent code representing the fractured object into a complete shape and a break surface. We show restorations for synthetic fractures from the Geometric Breaks and Breaking Bad datasets, and cultural heritage objects from the QP dataset, and for real fractures from the Fantastic Breaks dataset. We overcome challenges in restoring axially symmetric objects by predicting view-centered restorations. Our approach outperforms shape completion approaches adapted for shape repair in terms of chamfer distance, earth mover's distance, normal consistency, and percent restorations generated

Recycle-GAN: Unsupervised Video Retargeting

We introduce a data-driven approach for unsupervised video retargeting that translates content from one domain to another while preserving the style native to a domain, i.e., if contents of John Oliver's speech were to be transferred to Stephen Colbert, then the generated content/speech should be in Stephen Colbert's style. Our approach combines both spatial and temporal information along with adversarial losses for content translation and style preservation. In this work, we first study the advantages of using spatiotemporal constraints over spatial constraints for effective retargeting. We then demonstrate the proposed approach for the problems where information in both space and time matters such as face-to-face translation, flower-to-flower, wind and cloud synthesis, sunrise and sunset.Comment: ECCV 2018; Please refer to project webpage for videos - http://www.cs.cmu.edu/~aayushb/Recycle-GA

ECOFRIENDLY SYNTHESIS OF 2-PHENYL-3,5-DITHIO-7- SUBSTITUTEDIMINO-6-[2,4-DICHLORO-1,3,5-TRIAZ-6-YL]-1,2,4,6- THIATRIAZEPINE

ABSTRACT Recently in this laboratory a novel series 2-phenyl-3,5-dithio-7-substitutedimino-6-[2,4-dichloro-1,3,5-triaz-6-yl]-1,2,4,6-thiatriazepines (IIIa-e) were successfully synthesized by the interactions of 2,4-dichloro-6-[2,4-dithio-5-phenylbiureto]-1,3,5-triazines (I) with substituted isothiocarbomoyl chloride (IIa-e) in 1:1 molar ratio in acetone-ethanol medium. A new route was developed for the synthesis of thiatriazepines to increase the yield of product by maintaining the purity of the product, at the same time it was also thought to decrease the time span of the reaction. The achievement of this research work that we developed a new route for the synthesis of substituted-1,2,4,6-thiatriazepines by maintaining the purity as well as their is increases in the yield of the product. During synthesis two parameters of Green Chemistry are maintained hence, this is ecofriendly reaction. The justification and identification of the structure of these newly synthesized compounds had been established on the basis of chemical characterization, elemental analysis and through spectral data

Protoplast fusion studies in Ocimum species

Protoplasts of three Ocimum species, viz., O. basilicum, O. sanctum and O. gratissimum derived from leaf samples were used for studying fusion. The isolation of protoplasts were carried out using 1 % cellulase and 0.5 % pectinase in combination with Cocking-Peberdy-White (CPW)–13% mannitol solution as it gave the best results.  The protoplasts obtained by enzymatic digestion were purified by centrifugation.  The yield of protoplasts was found to be highest when centrifuged at 800 rpm for 15 minutes at 4ºC. Among  the  fusogen  combinations  tried  to  obtain  homokaryons,  40  %  Polyethylene glycol (PEG)-6000  fusogen solution gave better  results when  compared  to  the other  combinations. The Ca2+ concentration and pH level (5-9) were altered and studied but major changes in time taken for fusion were not observed.  The mean count of protoplasts per ml was found to be 209,200 for O. basilicum, 317,500 for O. gratissimum and 502,500 for O. sanctum. The Evans blue staining test showed that the average percentage of viable cells was 52.8 % for O. basilicum, 52.54 % for O. sanctum and 47.15 % for O. gratissimum

Fantastic Breaks: A Dataset of Paired 3D Scans of Real-World Broken Objects and Their Complete Counterparts

Automated shape repair approaches currently lack access to datasets that describe real-world damaged geometry. We present Fantastic Breaks (and Where to Find Them: https://terascale-all-sensing-research-studio.github.io/FantasticBreaks), a dataset containing scanned, waterproofed, and cleaned 3D meshes for 150 broken objects, paired and geometrically aligned with complete counterparts. Fantastic Breaks contains class and material labels, proxy repair parts that join to broken meshes to generate complete meshes, and manually annotated fracture boundaries. Through a detailed analysis of fracture geometry, we reveal differences between Fantastic Breaks and synthetic fracture datasets generated using geometric and physics-based methods. We show experimental shape repair evaluation with Fantastic Breaks using multiple learning-based approaches pre-trained with synthetic datasets and re-trained with subset of Fantastic Breaks.Comment: To be published at CVPR 202

AUTO3D: Novel view synthesis through unsupervisely learned variational viewpoint and global 3D representation

This paper targets on learning-based novel view synthesis from a single or limited 2D images without the pose supervision. In the viewer-centered coordinates, we construct an end-to-end trainable conditional variational framework to disentangle the unsupervisely learned relative-pose/rotation and implicit global 3D representation (shape, texture and the origin of viewer-centered coordinates, etc.). The global appearance of the 3D object is given by several appearance-describing images taken from any number of viewpoints. Our spatial correlation module extracts a global 3D representation from the appearance-describing images in a permutation invariant manner. Our system can achieve implicitly 3D understanding without explicitly 3D reconstruction. With an unsupervisely learned viewer-centered relative-pose/rotation code, the decoder can hallucinate the novel view continuously by sampling the relative-pose in a prior distribution. In various applications, we demonstrate that our model can achieve comparable or even better results than pose/3D model-supervised learning-based novel view synthesis (NVS) methods with any number of input views.Comment: ECCV 202