Video enhancement using adaptive spatio-temporal connective filter and piecewise mapping

This paper presents a novel video enhancement system based on an adaptive spatio-temporal connective (ASTC) noise filter and an adaptive piecewise mapping function (APMF). For ill-exposed videos or those with much noise, we first introduce a novel local image statistic to identify impulse noise pixels, and then incorporate it into the classical bilateral filter to form ASTC, aiming to reduce the mixture of the most two common types of noises - Gaussian and impulse noises in spatial and temporal directions. After noise removal, we enhance the video contrast with APMF based on the statistical information of frame segmentation results. The experiment results demonstrate that, for diverse low-quality videos corrupted by mixed noise, underexposure, overexposure, or any mixture of the above, the proposed system can automatically produce satisfactory results

Focusing and Compression of Ultrashort Pulses through Scattering Media

Light scattering in inhomogeneous media induces wavefront distortions which pose an inherent limitation in many optical applications. Examples range from microscopy and nanosurgery to astronomy. In recent years, ongoing efforts have made the correction of spatial distortions possible by wavefront shaping techniques. However, when ultrashort pulses are employed scattering induces temporal distortions which hinder their use in nonlinear processes such as in multiphoton microscopy and quantum control experiments. Here we show that correction of both spatial and temporal distortions can be attained by manipulating only the spatial degrees of freedom of the incident wavefront. Moreover, by optimizing a nonlinear signal the refocused pulse can be shorter than the input pulse. We demonstrate focusing of 100fs pulses through a 1mm thick brain tissue, and 1000-fold enhancement of a localized two-photon fluorescence signal. Our results open up new possibilities for optical manipulation and nonlinear imaging in scattering media

Online real-time crowd behavior detection in video sequences

Automatically detecting events in crowded scenes is a challenging task in Computer Vision. A number of offline approaches have been proposed for solving the problem of crowd behavior detection, however the offline assumption limits their application in real-world video surveillance systems. In this paper, we propose an online and real-time method for detecting events in crowded video sequences. The proposed approach is based on the combination of visual feature extraction and image segmentation and it works without the need of a training phase. A quantitative experimental evaluation has been carried out on multiple publicly available video sequences, containing data from various crowd scenarios and different types of events, to demonstrate the effectiveness of the approach

Locally-adapted convolution-based super-resolution of irregularly-sampled ocean remote sensing data

Super-resolution is a classical problem in image processing, with numerous applications to remote sensing image enhancement. Here, we address the super-resolution of irregularly-sampled remote sensing images. Using an optimal interpolation as the low-resolution reconstruction, we explore locally-adapted multimodal convolutional models and investigate different dictionary-based decompositions, namely based on principal component analysis (PCA), sparse priors and non-negativity constraints. We consider an application to the reconstruction of sea surface height (SSH) fields from two information sources, along-track altimeter data and sea surface temperature (SST) data. The reported experiments demonstrate the relevance of the proposed model, especially locally-adapted parametrizations with non-negativity constraints, to outperform optimally-interpolated reconstructions.Comment: 4 pages, 3 figure

Detecting and removing visual distractors for video aesthetic enhancement

Personal videos often contain visual distractors, which are objects that are accidentally captured that can distract viewers from focusing on the main subjects. We propose a method to automatically detect and localize these distractors through learning from a manually labeled dataset. To achieve spatially and temporally coherent detection, we propose extracting features at the Temporal-Superpixel (TSP) level using a traditional SVM-based learning framework. We also experiment with end-to-end learning using Convolutional Neural Networks (CNNs), which achieves slightly higher performance than other methods. The classification result is further refined in a post-processing step based on graph-cut optimization. Experimental results show that our method achieves an accuracy of 81% and a recall of 86%. We demonstrate several ways of removing the detected distractors to improve the video quality, including video hole filling; video frame replacement; and camera path re-planning. The user study results show that our method can significantly improve the aesthetic quality of videos

Spatio-temporal action localization with Deep Learning

Dissertação de mestrado em Engenharia InformáticaThe system that detects and identifies human activities are named human action recognition. On the video approach, human activity is classified into four different categories, depending on the complexity of the steps and the number of body parts involved in the action, namely gestures, actions, interactions, and activities, which is challenging for video Human action recognition to capture valuable and discriminative features because of the human body’s variations. So, deep learning techniques have provided practical applications in multiple fields of signal processing, usually surpassing traditional signal processing on a large scale. Recently, several applications, namely surveillance, human-computer interaction, and video recovery based on its content, have studied violence’s detection and recognition. In recent years there has been a rapid growth in the production and consumption of a wide variety of video data due to the popularization of high quality and relatively low-price video devices. Smartphones and digital cameras contributed a lot to this factor. At the same time, there are about 300 hours of video data updates every minute on YouTube. Along with the growing production of video data, new technologies such as video captioning, answering video surveys, and video-based activity/event detection are emerging every day. From the video input data, the detection of human activity indicates which activity is contained in the video and locates the regions in the video where the activity occurs. This dissertation has conducted an experiment to identify and detect violence with spatial action localization, adapting a public dataset for effect. The idea was used an annotated dataset of general action recognition and adapted only for violence detection.O sistema que deteta e identifica as atividades humanas é denominado reconhecimento da ação humana. Na abordagem por vídeo, a atividade humana é classificada em quatro categorias diferentes, dependendo da complexidade das etapas e do número de partes do corpo envolvidas na ação, a saber, gestos, ações, interações e atividades, o que é desafiador para o reconhecimento da ação humana do vídeo para capturar características valiosas e discriminativas devido às variações do corpo humano. Portanto, as técnicas de deep learning forneceram aplicações práticas em vários campos de processamento de sinal, geralmente superando o processamento de sinal tradicional em grande escala. Recentemente, várias aplicações, nomeadamente na vigilância, interação humano computador e recuperação de vídeo com base no seu conteúdo, estudaram a deteção e o reconhecimento da violência. Nos últimos anos, tem havido um rápido crescimento na produção e consumo de uma ampla variedade de dados de vídeo devido à popularização de dispositivos de vídeo de alta qualidade e preços relativamente baixos. Smartphones e cameras digitais contribuíram muito para esse fator. Ao mesmo tempo, há cerca de 300 horas de atualizações de dados de vídeo a cada minuto no YouTube. Junto com a produção crescente de dados de vídeo, novas tecnologias, como legendagem de vídeo, respostas a pesquisas de vídeo e deteção de eventos / atividades baseadas em vídeo estão surgindo todos os dias. A partir dos dados de entrada de vídeo, a deteção de atividade humana indica qual atividade está contida no vídeo e localiza as regiões no vídeo onde a atividade ocorre. Esta dissertação conduziu uma experiência para identificar e detetar violência com localização espacial, adaptando um dataset público para efeito. A ideia foi usada um conjunto de dados anotado de reconhecimento de ações gerais e adaptá-la apenas para deteção de violência

Motion Scalability for Video Coding with Flexible Spatio-Temporal Decompositions

PhDThe research presented in this thesis aims to extend the scalability range of the wavelet-based video coding systems in order to achieve fully scalable coding with a wide range of available decoding points. Since the temporal redundancy regularly comprises the main portion of the global video sequence redundancy, the techniques that can be generally termed motion decorrelation techniques have a central role in the overall compression performance. For this reason the scalable motion modelling and coding are of utmost importance, and specifically, in this thesis possible solutions are identified and analysed. The main contributions of the presented research are grouped into two interrelated and complementary topics. Firstly a flexible motion model with rateoptimised estimation technique is introduced. The proposed motion model is based on tree structures and allows high adaptability needed for layered motion coding. The flexible structure for motion compensation allows for optimisation at different stages of the adaptive spatio-temporal decomposition, which is crucial for scalable coding that targets decoding on different resolutions. By utilising an adaptive choice of wavelet filterbank, the model enables high compression based on efficient mode selection. Secondly, solutions for scalable motion modelling and coding are developed. These solutions are based on precision limiting of motion vectors and creation of a layered motion structure that describes hierarchically coded motion. The solution based on precision limiting relies on layered bit-plane coding of motion vector values. The second solution builds on recently established techniques that impose scalability on a motion structure. The new approach is based on two major improvements: the evaluation of distortion in temporal Subbands and motion search in temporal subbands that finds the optimal motion vectors for layered motion structure. Exhaustive tests on the rate-distortion performance in demanding scalable video coding scenarios show benefits of application of both developed flexible motion model and various solutions for scalable motion coding