A Federated Approach for Fine-Grained Classification of Fashion Apparel

As online retail services proliferate and are pervasive in modern lives, applications for classifying fashion apparel features from image data are becoming more indispensable. Online retailers, from leading companies to start-ups, can leverage such applications in order to increase profit margin and enhance the consumer experience. Many notable schemes have been proposed to classify fashion items, however, the majority of which focused upon classifying basic-level categories, such as T-shirts, pants, skirts, shoes, bags, and so forth. In contrast to most prior efforts, this paper aims to enable an in-depth classification of fashion item attributes within the same category. Beginning with a single dress, we seek to classify the type of dress hem, the hem length, and the sleeve length. The proposed scheme is comprised of three major stages: (a) localization of a target item from an input image using semantic segmentation, (b) detection of human key points (e.g., point of shoulder) using a pre-trained CNN and a bounding box, and (c) three phases to classify the attributes using a combination of algorithmic approaches and deep neural networks. The experimental results demonstrate that the proposed scheme is highly effective, with all categories having average precision of above 93.02%, and outperforms existing Convolutional Neural Networks (CNNs)-based schemes.Comment: 11 pages, 4 figures, 5 tables, submitted to IEEE ACCESS (under review

Structured Landmark Detection via Topology-Adapting Deep Graph Learning

Image landmark detection aims to automatically identify the locations of predefined fiducial points. Despite recent success in this field, higher-ordered structural modeling to capture implicit or explicit relationships among anatomical landmarks has not been adequately exploited. In this work, we present a new topology-adapting deep graph learning approach for accurate anatomical facial and medical (e.g., hand, pelvis) landmark detection. The proposed method constructs graph signals leveraging both local image features and global shape features. The adaptive graph topology naturally explores and lands on task-specific structures which are learned end-to-end with two Graph Convolutional Networks (GCNs). Extensive experiments are conducted on three public facial image datasets (WFLW, 300W, and COFW-68) as well as three real-world X-ray medical datasets (Cephalometric (public), Hand and Pelvis). Quantitative results comparing with the previous state-of-the-art approaches across all studied datasets indicating the superior performance in both robustness and accuracy. Qualitative visualizations of the learned graph topologies demonstrate a physically plausible connectivity laying behind the landmarks.Comment: Accepted to ECCV-20. Camera-ready with supplementary materia

Cognitive visual tracking and camera control

Cognitive visual tracking is the process of observing and understanding the behaviour of a moving person. This paper presents an efficient solution to extract, in real-time, high-level information from an observed scene, and generate the most appropriate commands for a set of pan-tilt-zoom (PTZ) cameras in a surveillance scenario. Such a high-level feedback control loop, which is the main novelty of our work, will serve to reduce uncertainties in the observed scene and to maximize the amount of information extracted from it. It is implemented with a distributed camera system using SQL tables as virtual communication channels, and Situation Graph Trees for knowledge representation, inference and high-level camera control. A set of experiments in a surveillance scenario show the effectiveness of our approach and its potential for real applications of cognitive vision

Multi-View Ontology Explorer (MOE): Interactive Visual Exploration of Ontologies

An ontology is an explicit specification of a conceptualization. This specification consists of a common vocabulary and information structure of a domain. Ontologies have applications in many fields to semantically link information in a standardized manner. In these fields, it is often crucial for both expert and non-expert users to quickly grasp the contents of an ontology; and to achieve this, many ontology tools implement visualization components. There are many past works on ontology visualization, and most of these tools are adapted from tree and graph based visualization techniques (e.g. treemaps, node-link graphs, and 3D interfaces). However, due to the enormous size of ontologies, these existing tools have their own shortcomings when dealing information overload, usually resulting in clutter and occlusion on the screen. In this thesis, we propose a set of novel visualizations and interactions to visualize very large ontologies. We design 5 dynamically linked visualizations that focus on a different level of abstraction individually. These different levels of abstraction start from a high-level overview down to a low-level entity. In addition, these visualizations collectively visualize landmarks, routes, and survey knowledge to support the formation of mental models. Search and save features are implemented to support on-demand and guided exploration. Finally, we implement our design as a web application

A Conceptual Model of Exploration Wayfinding: An Integrated Theoretical Framework and Computational Methodology

This thesis is an attempt to integrate contending cognitive approaches to modeling wayfinding behavior. The primary goal is to create a plausible model for exploration tasks within indoor environments. This conceptual model can be extended for practical applications in the design, planning, and Social sciences. Using empirical evidence a cognitive schema is designed that accounts for perceptual and behavioral preferences in pedestrian navigation. Using this created schema, as a guiding framework, the use of network analysis and space syntax act as a computational methods to simulate human exploration wayfinding in unfamiliar indoor environments. The conceptual model provided is then implemented in two ways. First of which is by updating an existing agent-based modeling software directly. The second means of deploying the model is using a spatial interaction model that distributed visual attraction and movement permeability across a graph-representation of building floor plans

Cloth2Tex: A Customized Cloth Texture Generation Pipeline for 3D Virtual Try-On

Fabricating and designing 3D garments has become extremely demanding with the increasing need for synthesizing realistic dressed persons for a variety of applications, e.g. 3D virtual try-on, digitalization of 2D clothes into 3D apparel, and cloth animation. It thus necessitates a simple and straightforward pipeline to obtain high-quality texture from simple input, such as 2D reference images. Since traditional warping-based texture generation methods require a significant number of control points to be manually selected for each type of garment, which can be a time-consuming and tedious process. We propose a novel method, called Cloth2Tex, which eliminates the human burden in this process. Cloth2Tex is a self-supervised method that generates texture maps with reasonable layout and structural consistency. Another key feature of Cloth2Tex is that it can be used to support high-fidelity texture inpainting. This is done by combining Cloth2Tex with a prevailing latent diffusion model. We evaluate our approach both qualitatively and quantitatively and demonstrate that Cloth2Tex can generate high-quality texture maps and achieve the best visual effects in comparison to other methods. Project page: tomguluson92.github.io/projects/cloth2tex/Comment: 15 pages, 15 figure

Self-Localization of Autonomous Vehicles Using Landmark Object Detection

Fully autonomous vehicles are rapidly approaching realization and concerns regarding their safety and robustness are a prominent obstacle to their integration into society. A bottleneck of an autonomous vehicle’s safety is its ability to self-localize under all types of conditions. Self-localization is critical for the vehicle to route to a destination, for the vehicle itself to be tracked in case of theft, and in determining local traffic laws to operate harmoniously with other traffic and pedestrians. The global positioning system (GPS) is utilized by autonomous vehicles for self-localization, but the availability and reliability of GPS is not guaranteed in all situations, such as when GPS reception is weak or when an adversary is spoofing information. We address this problem by proposing a self-localization method for autonomous vehicles that does not require GPS at localization time. The framework herein describes the representation of a road network as a graph using the data made available by OpenStreetMap, and an encoding of street segments based on detected landmark objects. We derive the current state observer transition function from the resulting graph, and employ localization methods based on set distance and minimum cost paths to determine the most probable location of the vehicle given a sequence of observed landmarks. Through simulating vehicle traveling paths along a road network generated from real data of a region of Washington D.C., we evaluate the performance of our method for varying degrees of landmark observation error and analyze the algorithm’s time and memory complexity, demonstrating that the approach provides a feasible solution to the problem