Linear color correction for multiple illumination changes and non-overlapping cameras

Many image processing methods, such as techniques for people re-identification, assume photometric constancy between different images. This study addresses the correction of photometric variations based upon changes in background areas to correct foreground areas. The authors assume a multiple light source model where all light sources can have different colours and will change over time. In training mode, the authors learn per-location relations between foreground and background colour intensities. In correction mode, the authors apply a double linear correction model based on learned relations. This double linear correction includes a dynamic local illumination correction mapping as well as an inter-camera mapping. The authors evaluate their illumination correction by computing the similarity between two images based on the earth mover's distance. The authors compare the results to a representative auto-exposure algorithm found in the recent literature plus a colour correction one based on the inverse-intensity chromaticity. Especially in complex scenarios the authors’ method outperforms these state-of-the-art algorithms

Segmentation and wake removal of seafaring vessels in optical satellite images

ABSTRACT This paper aims at the segmentation of seafaring vessels in optical satellite images, which allows an accurate length estimation. In maritime situation awareness, vessel length is an important parameter to classify a vessel. The proposed segmentation system consists of robust foreground-background separation, wake detection and ship-wake separation, simultaneous position and profile clustering and a special module for small vessel segmentation. We compared our system with a baseline implementation on 53 vessels that were observed with GeoEye-1. The results show that the relative L1 error in the length estimation is reduced from 3.9 to 0.5, which is an improvement of 87%. We learned that the wake removal is an important element for the accurate segmentation and length estimation of ships

Incremental concept learning with few training examples and hierarchical classification

Object recognition and localization are important to automatically interpret video and allow better querying on its content. We propose a method for object localization that learns incrementally and addresses four key aspects. Firstly, we show that for certain applications, recognition is feasible with only a few training samples. Secondly, we show that novel objects can be added incrementally without retraining existing objects, which is important for fast interaction. Thirdly, we show that an unbalanced number of positive training samples leads to biased classi er scores that can be corrected by modifying weights. Fourthly, we show that the detector performance can deteriorate due to hard-negative mining for similar or closely related classes (e.g., for Barbie and dress, because the doll is wearing a dress). This can be solved by our hierarchical classi cation. We introduce a new dataset, which we call TOSO, and use it to demonstrate the e ectiveness of the proposed method for the localization and recognition of multiple objects in images.This research was performed in the GOOSE project, which is jointly funded by the enabling technology program Adaptive Multi Sensor Networks (AMSN) and the MIST research program of the Dutch Ministry of Defense. This publication was supported by the research program Making Sense of Big Data (MSoBD).peer-reviewe

Interactive detection of incrementally learned concepts in images with ranking and semantic query interpretation

This research was performed in the GOOSE project, which is jointly funded by the MIST research program of the Dutch Ministry of Defense and the AMSN enabling technology program.The number of networked cameras is growing exponentially. Multiple applications in different domains result in an increasing need to search semantically over video sensor data. In this paper, we present the GOOSE demonstrator, which is a real-time general-purpose search engine that allows users to pose natural language queries to retrieve corresponding images. Top-down, this demonstrator interprets queries, which are presented as an intuitive graph to collect user feedback. Bottomup, the system automatically recognizes and localizes concepts in images and it can incrementally learn novel concepts. A smart ranking combines both and allows effective retrieval of relevant images.peer-reviewe

Recognition and localization of relevant human behavior in videos, SPIE,

ABSTRACT Ground surveillance is normally performed by human assets, since it requires visual intelligence. However, especially for military operations, this can be dangerous and is very resource intensive. Therefore, unmanned autonomous visualintelligence systems are desired. In this paper, we present an improved system that can recognize actions of a human and interactions between multiple humans. Central to the new system is our agent-based architecture. The system is trained on thousands of videos and evaluated on realistic persistent surveillance data in the DARPA Mind's Eye program, with hours of videos of challenging scenes. The results show that our system is able to track the people, detect and localize events, and discriminate between different behaviors, and it performs 3.4 times better than our previous system

TNO at TRECVID 2013 : multimedia event detection and instance search

We describe the TNO system and the evaluation results for TRECVID 2013 Multimedia Event Detection (MED) and instance search (INS) tasks. The MED system consists of a bag-of-word (BOW) approach with spatial tiling that uses low-level static and dynamic visual features, an audio feature and high-level concepts. Automatic speech recognition (ASR) and optical character recognition (OCR) are not used in the system. In the MED case with 100 example training videos, support-vector machines (SVM) are trained and fused to detect an event in the test set. In the case with 0 example videos, positive and negative concepts are extracted as keywords from the textual event description and events are detected with the high-level concepts. The MED results show that the SIFT keypoint descriptor is the one which contributes best to the results, fusion of multiple low-level features helps to improve the performance, and the textual event-description chain currently performs poorly. The TNO INS system presents a baseline open-source approach using standard SIFT keypoint detection and exhaustive matching. In order to speed up search times for queries a basic map-reduce scheme is presented to be used on a multi-node cluster. Our INS results show above-median results with acceptable search times.This research for the MED submission was performed in the GOOSE project, which is jointly funded by the enabling technology program Adaptive Multi Sensor Networks (AMSN) and the MIST research program of the Dutch Ministry of Defense. The INS submission was partly supported by the MIME project of the creative industries knowledge and innovation network CLICKNL.peer-reviewe

Vessel-diameter quantification and embolus detection in CTA images

Pulmonary embolism (PE) is the sudden obstruction of an artery in the lungs, usually due to a blood clot. There are more than 50 cases of PE per 100,000 persons every year in the USA. Of these cases, 11% die in the first hour and in total, the untreated mortality rate of PE is estimated to be 30%. Thus, PE is a common disorder with a high morbidity and mortality for which an early and precise diagnosis is highly desirable. Contrast-enhanced multi-slice x-ray computed tomography (CT) has become the preferred initial imaging test (and often the only test) to diagnose PE, because it is a simple, minimally invasive, fast and high-resolution imaging technique that allows the direct depiction of a clot inside the blood vessels. The CT image can also be used to identify other potentially life-threatening causes in a patient with chest pain. In contrast-enhanced CT (i.e., CT angiography, CTA) images, the blood vessels appear to be very bright because the contrast material is dissolved in blood. The embolus does not absorb contrast material, and therefore, it remains darker. PE can be recognized in CTA images as a dark area in the pulmonary arteries. However, manual detection of the dark spots that correspond to PE in CT images is often described by radiologists as difficult and time consuming. Therefore, computer-aided diagnosis (CAD) is desirable. In this thesis, we propose a new CAD system for automatic detection of PE in CTA images. The evaluation shows that the performance of our system is at the level of state of the art literature. The data was selected to demonstrate a variety of thrombus load, considerable breathing artifacts, sub-optimal contrast and parenchymal diseases, and none of the emboli were excluded for evaluation. This is important because the main problem of PE detection is the separation between true PE and look-alikes, which is much harder when the patient is not healthy. The CAD system that we propose consists of several steps. In the first step, pulmonary vessels are segmented and PE candidates are detected inside the vessel segmentation. The candidate detection step focusses on the inclusion of PE – even when vessels are completely occluded – and the exclusion of false detections, such as lymphoid tissue and parenchymal diseases. Subsequently, features are computed on each of the candidates to enable classification of the candidates. The feature-computation step does not only focus on the intensity, shape and size of an embolus, but also on relative locations and the regular shape of the pulmonary vascular tree. In the last step, classification is used to separate candidates that represent real emboli from the other candidates. The system is optimized with feature selection and classifier selection. Several classifiers have been tested and the results show that the performance is optimized by using a bagged tree classifier with the features distance-to-parenchyma and stringness. The system was trained on 38 CT data sets. Evaluation on 19 other data sets showed that the system generalizes well. The sensitivity of our system on the evaluation data is 63% at 4.9 false positives per data set, which allowed the radiologist to improve the number of detected PE with 22%. Another part of this thesis is about the accurate quantification of the vessel diameter in CT images. Quantification of the local vessel diameter is essential for the correct diagnosis of vascular diseases. For example, the relative decrease in diameter of a stenosis is an important factor in determining the treatment therapy. However, inherent to image acquisition is a blurring effect, which causes a bias in the diameter estimation of most methods. In this thesis, we focus on fast and accurate (unbiased) vessel-diameter quantification. For the localization of the vessel wall, Gaussian derivatives are often used as differential operators. We show how these Gaussian derivatives should be computed on multi-dimensional data with anisotropic voxels and anisotropic blurring. The voxels and blurring are usually anisotropic in the 3D CT images, which means that the voxel size and the amount of blur is not equal in all three directions. Furthermore, we show that the computational cost of interpolation and differentiation on Gaussian blurred images can be reduced by using B-spline interpolation and approximation, without losing accuracy. We introduce a derivative-based edge detector with unbiased localization on curved surfaces in spite of the blur in CT images. Finally, we propose a modification of the full-width at half-maximum (FWHM) criterion to create an unbiased method for vessel-diameter quantification in CT images. This criterion is not only cheaper but also more robust to noise than the commonly used derivative-based edge detectors