Representation Learning for Shape Decomposition, By Shape Decomposition

The ability to parse 3D objects into their constituent parts is essential for humans to understand and interact with the surrounding world. Imparting this skill in machines is important for various computer graphics, computer vision, and robotics tasks. Machines endowed with this skill can better interact with its surroundings, perform shape editing, texturing, recomposing, tracking, and animation. In this thesis, we ask two questions. First, how can machines decompose 3D shapes into their fundamental parts? Second, does the ability to decompose the 3D shape into these parts help learn useful 3D shape representations? In this thesis, we focus on parsing the shape into compact representations, such as parametric surface patches and Constructive Solid Geometry (CSG) primitives, which are also widely used representations in 3D modeling in computer graphics. Inspired by the advances in neural networks for 3D shape processing, we develop neural network approaches to tackle shape decomposition. First, we present CSGNet, a network architecture to parse shapes into CSG programs, which is trained using combination of supervised and reinforcement learning. Second, we present ParSeNet, a network architecture to decompose a shape into parametric surface patches (B-Spline) and geometric primitives (plane, cone, cylinder and sphere), trained on a large set of CAD models using supervised learning. The training of deep neural network architectures for 3D recognition and generation tasks requires a large amount of labeled datasets. We explore ways to alleviate this problem by relying on shape decomposition methods to guide the learning process. Towards that end, we first study the use of freely available metadata, albeit inconsistent, from shape repositories to learn 3D shape features. Later we show that learning to decompose a 3D shape into geometric primitives also helps in learning shape representations useful for semantic segmentation tasks. Finally, since most 3D shapes encountered in real life are textured, consisting of several fine-grained semantic parts, we propose a method to learn fine-grained representations for textured 3D shapes in a self-supervised manner by incorporating 3D geometric priors

CSGNet: Neural Shape Parser for Constructive Solid Geometry

We present a neural architecture that takes as input a 2D or 3D shape and outputs a program that generates the shape. The instructions in our program are based on constructive solid geometry principles, i.e., a set of boolean operations on shape primitives defined recursively. Bottom-up techniques for this shape parsing task rely on primitive detection and are inherently slow since the search space over possible primitive combinations is large. In contrast, our model uses a recurrent neural network that parses the input shape in a top-down manner, which is significantly faster and yields a compact and easy-to-interpret sequence of modeling instructions. Our model is also more effective as a shape detector compared to existing state-of-the-art detection techniques. We finally demonstrate that our network can be trained on novel datasets without ground-truth program annotations through policy gradient techniques.Comment: Accepted at CVPR-201

Analysis and interpretation of forest fire data of Sikkim

Forest ecosystems are depleting and heading towards degradation which would adversely affect the world's socio-economic harmony. Various disasters disturb the cordial relationship of the flora and fauna and impose imbalance in the ecology as a whole; forest fire is one of its kind. India has witnessed a 125% rise in forest fire occurrences between the years 2015 and 2017. This paper presents a study of various factors and the analysis of forest fire in Sikkim. The period of 10 years, forest fire incidences, i.e., from the year 2004 to the year 2014 have been considered for the study. The forest fire data was collected from Forest and Environment Department, Government of Sikkim, and preliminary processing was performed to check for anomalies. The study observed that there has been an increased forest fire incidence over the years and highest being in the year 2009. These fire incidences have damaged a total area of 5,047.16 ha of land damaging various flora and fauna. It was observed that the maximum forest fire cases are below an altitude of 1500m, during winter months (December to February extending to March) and in sub-tropical Sal (Shorea robusta) forest. West district of Sikkim recorded the highest number of forest fire incidences and area covered followed by south and east districts; the north district was least affected. As per the visual interpretation of forest fire incidence data and literature review, the main factors responsible for forest fire in Sikkim are low rainfall, dry winter season, and type of vegetation. Also, a linear regression was performed between weather factors like average temperature (°C), relative humidity (%), and wind velocity (Km/h) on incidences of forest fire between the year 2009-2014 (n=389). It was found that the average temperature (r=0.37, Slope=9.59 and SD= ±12.00) and relative humidity (r=-0.6, Slope=-4.52, and SD=±2.68) plays a moderate linear relationship in influencing the incidences of forest fires. However, wind velocity showed almost a flat curve indicating its minimal role in influencing forest fire incidences. Parameter modelling and preparation of forest fire risk zone map would be an effective tool in preventing and managing forest fire in Sikkim

Sutures versus staplers for skin closure in laparotomy patients: a prospective study

Background: Every surgeon wants cosmetically acceptable scars along with optimal healing. Good tissue union and cosmetically acceptable scars are vital for ideal surgical practice. A basic need for skin closure is a good approximation. Apart from cosmetically good scars, it is also necessary that the skin closure technique should be technically easy, speedy, economical and acceptable.Methods: The study was conducted on 100 patients on whom elective abdominal surgeries were performed. Patients were divided into two groups with 50 patients in each group after matching the parameters like age, co morbid conditions, using simple random sampling technique. All operations were performed by one consultant. In group A, Skin was approximated with vertical mattress sutures while in group B, staplers were used to close the wound.Results: The age of the patients varied from 16 to 85 years. The average time taken for skin closure for suture group (A) was found to be 300sec (±20.78) and for stapler group was found to be 120sec (±16.50) respectively. Wound infection was found in 10 patients (20%). In stapler group 4 (8%) and in suture group 6 Patients (12%) had post-operative wound infection.Conclusions: Cosmesis is essential and necessary in modern surgical practice. It also reflects surgical expertise

Breaking of Simplified Data Encryption Standard using Genetic Algorithm

Cryptanalysis of ciphertext by using evolutionary algorithm has gained so much interest in recent years. In this paper we have used a Genetic algorithm with improved crossover operator (Ring Crossover) for cryptanalysis of SDES. There so many attacks in cryptography. The cipher text attack only is considered here and several keys are generated in the different run of the genetic algorithm on the basis of their cost function value which depends upon frequency of the letters. The results on the S-DES indicate that, this is a promising method and can be adopted to handle other complex block ciphers like DES, AES

Secondary recurrent miscarriage and sex of previous child

Background: Secondary Recurrent Miscarriage (SRM) is defined as occurrence of three or more spontaneous consecutive abortions following the birth of one child. Mothers of boys often get immunized against male-specific minor histocompatibility (H-Y) antigens due to transfer of fetal cells into the maternal circulation. The birth of a boy is significantly more common than a girl prior to secondary recurrent miscarriage (SRM) and is known to be associated with a poorer chance of a subsequent live birth. Children born after Secondary Recurrent Miscarriage are more likely to be girls. Aberrant H-Y immunity may be a causal factor for SRM.Methods: The study was conducted over a period of one year from January 2012 to December 2012. All the women presenting with full term pregnancy and previous history of unexplained, three or more spontaneous consecutive abortions at 8 to 20 weeks gestation were taken as cases. Sex of the previous child was sought. The patients were followed till delivery and sex of the child born subsequently was also noted. Association of history of SRM to sex of the previous and present child was calculated by appropriates statistical methods.Results: A total of 34 patients with history of SRM were studied. 23 out of them had a previous male child and 11 had given birth to a female (p=0.004). The male: female sex ratio of children born prior and subsequent to SRM was 2.09 and 0.79 respectively.Conclusions: Study supports the hypothesis that aberrant maternal H-Y immune response may have a pathogenic role in SRM