Contributions to region-based image and video analysis: feature aggregation, background subtraction and description constraining

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Tecnología Electrónica y de las Comunicaciones. Fecha de lectura: 22-01-2016Esta tesis tiene embargado el acceso al texto completo hasta el 22-07-2017The use of regions for image and video analysis has been traditionally motivated by their ability to diminish the number of processed units and hence, the number of required decisions. However, as we explore in this thesis, this is just one of the potential advantages that regions may provide. When dealing with regions, two description spaces may be differentiated: the decision space, on which regions are shaped—region segmentation—, and the feature space, on which regions are used for analysis—region-based applications—. These two spaces are highly related. The solutions taken on the decision space severely affect their performance in the feature space. Accordingly, in this thesis we propose contributions on both spaces. Regarding the contributions to region segmentation, these are two-fold. Firstly, we give a twist to a classical region segmentation technique, the Mean-Shift, by exploring new solutions to automatically set the spectral kernel bandwidth. Secondly, we propose a method to describe the micro-texture of a pixel neighbourhood by using an easily customisable filter-bank methodology—which is based on the discrete cosine transform (DCT)—. The rest of the thesis is devoted to describe region-based approaches to several highly topical issues in computer vision; two broad tasks are explored: background subtraction (BS) and local descriptors (LD). Concerning BS, regions are here used as complementary cues to refine pixel-based BS algorithms: by providing robust to illumination cues and by storing the background dynamics in a region-driven background modelling. Relating to LD, the region is here used to reshape the description area usually fixed for local descriptors. Region-masked versions of classical two-dimensional and three-dimensional local descriptions are designed. So-built descriptions are proposed for the task of object identification, under a novel neural-oriented strategy. Furthermore, a local description scheme based on a fuzzy use of the region membership is derived. This characterisation scheme has been geometrically adapted to account for projective deformations, providing a suitable tool for finding corresponding points in wide-baseline scenarios. Experiments have been conducted for every contribution, discussing the potential benefits and the limitations of the proposed schemes. In overall, obtained results suggest that the region—conditioned by successful aggregation processes—is a reliable and useful tool to extrapolate pixel-level results, diminish semantic noise, isolate significant object cues and constrain local descriptions. The methods and approaches described along this thesis present alternative or complementary solutions to pixel-based image processing.El uso de regiones para el análisis de imágenes y secuencias de video ha estado tradicionalmente motivado por su utilidad para disminuir el número de unidades de análisis y, por ende, el número de decisiones. En esta tesis evidenciamos que esta es sólo una de las muchas ventajas adheridas a la utilización de regiones. En el procesamiento por regiones deben distinguirse dos espacios de análisis: el espacio de decisión, en donde se construyen las regiones, y el espacio de características, donde se utilizan. Ambos espacios están altamente relacionados. Las soluciones diseñadas para la construcción de regiones en el espacio de decisión definen su utilidad en el espacio de análisis. Por este motivo, a lo largo de esta tesis estudiamos ambos espacios. En particular, proponemos dos contribuciones en la etapa de construcción de regiones. En la primera, revisitamos una técnica clásica, Mean-Shift, e introducimos un esquema para la selección automática del ancho de banda que permite estimar localmente la densidad de una determinada característica. En la segunda, utilizamos la transformada discreta del coseno para describir la variabilidad local en el entorno de un píxel. En el resto de la tesis exploramos soluciones en el espacio de características, en otras palabras, proponemos aplicaciones que se apoyan en la región para realizar el procesamiento. Dichas aplicaciones se centran en dos ramas candentes en el ámbito de la visión por computador: la segregación del frente por substracción del fondo y la descripción local de los puntos de una imagen. En la rama substracción de fondo, utilizamos las regiones como unidades de apoyo a los algoritmos basados exclusivamente en el análisis a nivel de píxel. En particular, mejoramos la robustez de estos algoritmos a los cambios locales de iluminación y al dinamismo del fondo. Para esta última técnica definimos un modelo de fondo completamente basado en regiones. Las contribuciones asociadas a la rama de descripción local están centradas en el uso de la región para definir, automáticamente, entornos de descripción alrededor de los puntos. En las aproximaciones existentes, estos entornos de descripción suelen ser de tamaño y forma fija. Como resultado de este procedimiento se establece el diseño de versiones enmascaradas de descriptores bidimensionales y tridimensionales. En el algoritmo desarrollado, organizamos los descriptores así diseñados en una estructura neuronal y los utilizamos para la identificación automática de objetos. Por otro lado, proponemos un esquema de descripción mediante asociación difusa de píxeles a regiones. Este entorno de descripción es transformado geométricamente para adaptarse a potenciales deformaciones proyectivas en entornos estéreo donde las cámaras están ampliamente separadas. Cada una de las aproximaciones desarrolladas se evalúa y discute, remarcando las ventajas e inconvenientes asociadas a su utilización. En general, los resultados obtenidos sugieren que la región, asumiendo que ha sido construida de manera exitosa, es una herramienta fiable y de utilidad para: extrapolar resultados a nivel de pixel, reducir el ruido semántico, aislar las características significativas de los objetos y restringir la descripción local de estas características. Los métodos y enfoques descritos a lo largo de esta tesis establecen soluciones alternativas o complementarias al análisis a nivel de píxelIt was partially supported by the Spanish Government trough its FPU grant program and the projects (TEC2007-65400 - SemanticVideo), (TEC2011-25995 Event Video) and (TEC2014-53176-R HAVideo); the European Commission (IST-FP6-027685 - Mesh); the Comunidad de Madrid (S-0505/TIC-0223 - ProMultiDis-CM) and the Spanish Administration Agency CENIT 2007-1007 (VISION)

Automatic semantic parsing of the ground-plane in scenarios recorded with multiple moving cameras

Nowadays, video surveillance scenarios usually rely on manually annotated focus areas to constrain automatic video analysis tasks. Whereas manual annotation simplifies several stages of the analysis, its use hinders the scalability of the developed solutions and might induce operational problems in scenarios recorded with Multiple and Moving Cameras (MMC). To tackle these problems, an automatic method for the cooperative extraction of Areas of Interest (AoIs) is proposed. Each captured frame is segmented into regions with semantic roles using a stateof- the-art method. Semantic evidences from different junctures, cameras and points-of-view are then spatio-temporally aligned on a common ground plane. Experimental results on widely-used datasets recorded with multiple but static cameras suggest that this process provides broader and more accurate AoIs than those manually defined in the datasets. Moreover, the proposed method naturally determines the projection of obstacles and functional objects in the scene, paving the road towards systems focused on the automatic analysis of human behaviour. To our knowledge, this is the first study dealing with this problematic, as evidenced by the lack of publicly available MMC benchmarks. To also cope with this issue, we provide a new MMC dataset with associated semantic scene annotationsThis study has been partially supported by the Spanish Government through its TEC2014-53176-R HAVideo projec

Accurate segmentation and registration of skin lesion images to evaluate lesion change

Skin cancer is a major health problem. There are several techniques to help diagnose skin lesions from a captured image. Computer-aided diagnosis (CAD) systems operate on single images of skin lesions, extracting lesion features to further classify them and help the specialists. Accurate feature extraction, which later on depends on precise lesion segmentation, is key for the performance of these systems. In this paper, we present a skin lesion segmentation algorithm based on a novel adaptation of superpixels techniques and achieve the best reported results for the ISIC 2017 challenge dataset. Additionally, CAD systems have paid little attention to a critical criterion in skin lesion diagnosis: the lesion's evolution. This requires operating on two or more images of the same lesion, captured at different times but with a comparable scale, orientation, and point of view; in other words, an image registration process should first be performed. We also propose in this work, an image registration approach that outperforms top image registration techniques. Combined with the proposed lesion segmentation algorithm, this allows for the accurate extraction of features to assess the evolution of the lesion. We present a case study with the lesion-size feature, paving the way for the development of automatic systems to easily evaluate skin lesion evolutionThis work was supported in part by the Spanish Government (HAVideo, TEC2014-53176-R) and in part by the TEC department (Universidad Autonoma de Madrid

Semantic-Aware Scene Recognition

Scene recognition is currently one of the top-challenging research fields in computer vision. This may be due to the ambiguity between classes: images of several scene classes may share similar objects, which causes confusion among them. The problem is aggravated when images of a particular scene class are notably different. Convolutional Neural Networks (CNNs) have significantly boosted performance in scene recognition, albeit it is still far below from other recognition tasks (e.g., object or image recognition). In this paper, we describe a novel approach for scene recognition based on an end-to-end multi-modal CNN that combines image and context information by means of an attention module. Context information, in the shape of semantic segmentation, is used to gate features extracted from the RGB image by leveraging on information encoded in the semantic representation: the set of scene objects and stuff, and their relative locations. This gating process reinforces the learning of indicative scene content and enhances scene disambiguation by refocusing the receptive fields of the CNN towards them. Experimental results on four publicly available datasets show that the proposed approach outperforms every other state-of-the-art method while significantly reducing the number of network parameters. All the code and data used along this paper is available at https://github.com/vpulab/Semantic-Aware-Scene-RecognitionComment: Paper submitted for publication to Elsevier Pattern Recognition journa

Semantic-aware scene recognition

Scene recognition is currently one of the top-challenging research fields in computer vision. This may be due to the ambiguity between classes: images of several scene classes may share similar objects, which causes confusion among them. The problem is aggravated when images of a particular scene class are notably different. Convolutional Neural Networks (CNNs) have significantly boosted performance in scene recognition, albeit it is still far below from other recognition tasks (e.g., object or image recognition). In this paper, we describe a novel approach for scene recognition based on an end-to-end multi-modal CNN that combines image and context information by means of an attention module. Context information, in the shape of a semantic segmentation, is used to gate features extracted from the RGB image by leveraging on information encoded in the semantic representation: the set of scene objects and stuff, and their relative locations. This gating process reinforces the learning of indicative scene content and enhances scene disambiguation by refocusing the receptive fields of the CNN towards them. Experimental results on three publicly available datasets show that the proposed approach outperforms every other state-of-the-art method while significantly reducing the number of network parameters. All the code and data used along this paper is available at: https://github.com/vpulab/Semantic-Aware-Scene-RecognitionThis study has been partially supported by the Spanish Government through its TEC2017-88169-R MobiNetVideo projec

Graph Convolutional Network for Multi-Target Multi-Camera Vehicle Tracking

This letter focuses on the task of Multi-Target Multi-Camera vehicle tracking. We propose to associate single-camera trajectories into multi-camera global trajectories by training a Graph Convolutional Network. Our approach simultaneously processes all cameras providing a global solution, and it is also robust to large cameras unsynchronizations. Furthermore, we design a new loss function to deal with class imbalance. Our proposal outperforms the related work showing better generalization and without requiring ad-hoc manual annotations or thresholds, unlike compared approaches

On exploring weakly supervised domain adaptation strategies for semantic segmentation using synthetic data

Pixel-wise image segmentation is key for many Computer Vision applications. The training of deep neural networks for this task has expensive pixel-level annotation requirements, thus, motivating a growing interest on synthetic data to provide unlimited data and its annotations. In this paper, we focus on the generation and application of synthetic data as representative training corpuses for semantic segmentation of urban scenes. First, we propose a synthetic data generation protocol, which identifies key features affecting performance and provides datasets with variable complexity. Second, we adapt two popular weakly supervised domain adaptation approaches (combined training, fine-tuning) to employ synthetic and real data. Moreover, we analyze several backbone models, real/synthetic datasets and their proportions when combined. Third, we propose a new curriculum learning strategy to employ several synthetic and real datasets. Our major findings suggest the high performance impact of pace and order of synthetic and real data presentation, achieving state of the art results for well-known models. The results by training with the proposed dataset outperform popular alternatives, thus demonstrating the effectiveness of the proposed protocol. Our code and dataset are available at http://www-vpu.eps.uam.es/publications/WSDA_semantic/Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work is part of the preliminary tasks related to the SEGA-CV (TED2021-131643A-I00) and the HVD (PID2021-125051OB-I00) projects funded by the Ministerio de Ciencia e Innovacion of the Spanish Governmen