http://disciplinas.stoa.usp.br/pluginfile.php/166516/mod_resource/content/1/Radiomics.pdf

Radiomics: the process and the challenges

In this paper, we consider the problem of recovering the spatial layout of indoor scenes from monocular images. The presence of clutter is a major problem for existing single-view 3D reconstruction algorithms, most of which rely on finding the ground-wall boundary. In most rooms, this boundary is partially or entirely occluded. We gain robustness to clutter by modeling the global room space with a parameteric 3D “box” and by iteratively localizing clutter and refitting the box. To fit the box, we introduce a structured learning algorithm that chooses the set of parameters to minimize error, based on global perspective cues. On a dataset of 308 images, we demonstrate the ability of our algorithm to recover spatial layout in cluttered rooms and show several examples of estimated free space.

/pdf/recovering-the-spatial-layout-of-cluttered-rooms-36l7h6g13p.pdf

Recovering the spatial layout of cluttered rooms

http://www4.comp.polyu.edu.hk/~cslzhang/paper/PR_2012_review.pdf

A survey of graph theoretical approaches to image segmentation

Both image segmentation and dense 3D modeling from images represent an intrinsically ill-posed problem. Strong regularizers are therefore required to constrain the solutions from being 'too noisy'. Unfortunately, these priors generally yield overly smooth reconstructions and/or segmentations in certain regions whereas they fail in other areas to constrain the solution sufficiently. In this paper we argue that image segmentation and dense 3D reconstruction contribute valuable information to each other's task. As a consequence, we propose a rigorous mathematical framework to formulate and solve a joint segmentation and dense reconstruction problem. Image segmentations provide geometric cues about which surface orientations are more likely to appear at a certain location in space whereas a dense 3D reconstruction yields a suitable regularization for the segmentation problem by lifting the labeling from 2D images to 3D space. We show how appearance-based cues and 3D surface orientation priors can be learned from training data and subsequently used for class-specific regularization. Experimental results on several real data sets highlight the advantages of our joint formulation.

/pdf/joint-3d-scene-reconstruction-and-class-segmentation-53qw0ew60c.pdf

Joint 3D Scene Reconstruction and Class Segmentation

With the rise and development of deep learning, computer vision has been tremendously transformed and reshaped. As an important research area in computer vision, scene text detection and recognition has been inevitably influenced by this wave of revolution, consequentially entering the era of deep learning. In recent years, the community has witnessed substantial advancements in mindset, methodology and performance. This survey is aimed at summarizing and analyzing the major changes and significant progresses of scene text detection and recognition in the deep learning era. Through this article, we devote to: (1) introduce new insights and ideas; (2) highlight recent techniques and benchmarks; (3) look ahead into future trends. Specifically, we will emphasize the dramatic differences brought by deep learning and remaining grand challenges. We expect that this review paper would serve as a reference book for researchers in this field. Related resources are also collected in our Github repository (
 https://github.com/Jyouhou/SceneTextPapers
 ).

Scene Text Detection and Recognition: The Deep Learning Era

Text that appears in images contains important and useful information. Detection and extraction of text in images have been used in many applications. In this paper, we propose a multiscale edge-based text extraction algorithm, which can automatically detect and extract text in complex images. The proposed method is a general-purpose text detection and extraction algorithm, which can deal not only with printed document images but also with scene text. It is robust with respect to the font size, style, color, orientation, and alignment of text and can be used in a large variety of application fields, such as mobile robot navigation, vehicle license detection and recognition, object identification, document retrieving, page segmentation, etc.

/pdf/multiscale-edge-based-text-extraction-from-complex-images-1dq66zn44u.pdf

Multiscale Edge-Based Text Extraction from Complex Images

Scene text is an important feature to be extracted, especially in vision-based mobile robot navigation as many potential landmarks such as nameplates and information signs contain text. This paper proposes an edge-based text region extraction algorithm, which is robust with respect to font sizes, styles, color/intensity, orientations, effects of illumination, reflections, shadows, perspective distortion, and the complexity of image backgrounds. Performance of the proposed algorithm is compared against a number of widely used text localization algorithms and the results show that this method can quickly and effectively localize and extract text regions from real scenes and can be used in mobile robot navigation under an indoor environment to detect text based landmarks.

An edge-based text region extraction algorithm for indoor mobile robot navigation

In the last decade, graph-cut optimization has been popular for a variety of pixel labeling problems. Typically graph-cut methods are used to incorporate a smoothness prior on a labeling. Recently several methods incorporated ordering constraints on labels for the application of object segmentation. An example of an ordering constraint is prohibiting a pixel with a ldquocar wheelrdquo label to be above a pixel with a ldquocar roofrdquo label. We observe that the commonly used graph-cut based alpha-expansion is more likely to get stuck in a local minimum when ordering constraints are used. For certain models with ordering constraints, we develop new graph-cut moves which we call order-preserving moves. Order-preserving moves act on all labels, unlike alpha-expansion. Although the global minimum is still not guaranteed, optimization with order-preserving moves performs significantly better than alpha-expansion. We evaluate order-preserving moves for the geometric class scene labeling (introduced by Hoiem et al.) where the goal is to assign each pixel a label such as ldquoskyrdquo, ldquogrounrdquo, etc., so ordering constraints arise naturally. In addition, we use order-preserving moves for certain simple shape priors in graphcut segmentation, which is a novel contribution in itself.

/pdf/graph-cut-with-ordering-constraints-on-labels-and-its-1skg463gpj.pdf

Graph cut with ordering constraints on labels and its applications

To protect naval and commercial ships from attack by terrorists and pirates, it is important to have automatic surveillance
systems able to detect, identify, track and alert the crew on small watercrafts that might pursue malicious intentions,
while ruling out non-threat entities. Radar systems have limitations on the minimum detectable range and lack high-level
classification power. In this paper, we present an innovative Automated Intelligent Video Surveillance System for Ships
(AIVS3) as a vision-based solution for ship security. Capitalizing on advanced computer vision algorithms and practical
machine learning methodologies, the developed AIVS3 is not only capable of efficiently and robustly detecting,
classifying, and tracking various maritime targets, but also able to fuse heterogeneous target information to interpret
scene activities, associate targets with levels of threat, and issue the corresponding alerts/recommendations to the man-in-
the-loop (MITL). AIVS3 has been tested in various maritime scenarios and shown accurate and effective threat
detection performance. By reducing the reliance on human eyes to monitor cluttered scenes, AIVS3 will save the
manpower while increasing the accuracy in detection and identification of asymmetric attacks for ship protection.

Automated intelligent video surveillance system for ships

In the last decade, graph-cut optimization has been popular for a variety of labeling problems. Typically, graph-cut methods are used to incorporate smoothness constraints on a labeling, encouraging most nearby pixels to have equal or similar labels. In addition to smoothness, ordering constraints on labels are also useful. For example, in object segmentation, a pixel with a “car wheel” label may be prohibited above a pixel with a “car roof” label. We observe that the commonly used graph-cut \alpha-expansion move algorithm is more likely to get stuck in a local minimum when ordering constraints are used. For a certain model with ordering constraints, we develop new graph-cut moves which we call order-preserving. The advantage of order-preserving moves is that they act on all labels simultaneously, unlike \alpha-expansion. More importantly, for most labels \alpha, the set of \alpha-expansion moves is strictly smaller than the set of order-preserving moves. This helps to explain why in practice optimization with order-preserving moves performs significantly better than \alpha-expansion in the presence of ordering constraints. We evaluate order-preserving moves for the geometric class scene labeling (introduced by Hoiem et al.) where the goal is to assign each pixel a label such as “sky,” “ground,” etc., so ordering constraints arise naturally. In addition, we use order-preserving moves for certain simple shape priors in graph-cut segmentation, which is a novel contribution in itself.

/pdf/order-preserving-moves-for-graph-cut-based-optimization-oywarcah8q.pdf

Xiaoqing Liu

Papers

Multiscale Edge-Based Text Extraction from Complex Images

An edge-based text region extraction algorithm for indoor mobile robot navigation

Graph cut with ordering constraints on labels and its applications

Automated intelligent video surveillance system for ships

Order-Preserving Moves for Graph-Cut-Based Optimization