Given blurred observations of a stationary scene captured using a static camera but with diﬀerent and unknown light source positions, we estimate the light source positions and scenestructure (surface gradients) and perform blind image restoration. The images are restored using the estimated light source positions, surface gradients, and albedo. The surface of the object is assumed to be Lambertian. We first propose a simple approach to obtain a rough estimate of the light source position from a single image using the shading information which does not use any calibration or initialization. We model the prior information for the scenestructure as a separate Markov random field (MRF) with discontinuity preservation, and the blur function is modeled as Gaussian. A proper regularization approach is then used to estimate the light source position, scenestructure, and blur parameter. The optimization is carried out using the graph cuts approach. The advantage of the proposed approach is that its time complexity is much less as compared to other approaches that use global optimization techniques such as simulated annealing. Reducing the time complexity is crucial in many of the practical vision problems. Results of experimentation on both synthetic and real images are presented.
Analyzing the depth structure implied in two-dimensional images is one of the most active research areas in computer vision. Here, we propose a method of utilizing texture within an image to derive its depth structure. Though most approaches for deriving depth from a single still image utilize luminance edges and shading to estimate scenestructure, relatively little work has been done to utilize the abundant texture information in images. Our new approach begins by analyzing the two cues of local spatial frequency and orientation distributions of the textures within an image, which are used to compute the local slant information across the image. The slant and frequency information are merged to create a unified depth map, providing an important channel for image structure information that can be combined with other available cues. The capabilities of the algorithm are illustrated for a variety of images of planar and curved surfaces under perspective projection, in most of which the depth structure is effortlessly perceived by human observers. Since these operations are readily implementable in neural hardware in early visual cortex, they therefore represent a model of the human perception of the depth structure of images from texture gradient cues.
Geographically and physically accurate models of man-made structures are used as the basis of many modeling applications. Automatically generating these models from aerial imagery would provide new opportunities for applications and research. The automated extraction of these types of models is split into two major sections; 1) the automated extraction of the geographically accurate structure using imagery, and 2) the modeling and analysis of this extracted structure. The computer vision community has developed a strong understanding of automated structure extraction processes, through a workflow known as Structure from Motion (SfM). The development of this understanding has pro- duced many methods for implementing this workflow, including processes that have been made open source and available to the public. Geographic accuracy requires knowledge of additional geographic information from the imaging platform. The use of aerial imagery provides a distinct advantage is this area, due to the common usage of highly accurate INS and GPS systems which record positional information for each image. In addition to this, aerial imaging platforms tend to be very well calibrated and characterized. These attributes make aerial imagery an ideal candidate for geographically accurate and auto- mated structure extraction.
In the first battery of experiments we explore the scene with a hand-held RGB-D sensor, building progressively a PbMap while at the same time, the system searches for places visited previously. In order to build the PbMap, the pose of each frame is estimated with a method for dense visual odometry (also called direct registration) . This method estimates the relative pose between two consecu- tive RGB-D observations by iteratively maximizing the photoconsistency of both images. The optimization is carried out in a coarse-to-fine scheme that improves e ffi- ciency and allows coping with larger di fferences between poses. The drift of this algorithm along the trajectory is su fficiently small to achieve locally accurate PbMaps. While the scene is explored and the PbMap is built, the current place is continuously searched in a set of 15 previ- ously acquired PbMaps corresponding to di fferent rooms of o ffice and home scenarios (these PbMaps generally capture a 360 ◦ coverage of the scene, see figure 8). An
Each of these cases leads to a different strategy for estimat- ing the scene flow. It seems intuitive that less knowledge of scenestructure requires the use of more optical flows, and indeed this result does follow from the amount of de- generacy in the linear equations used to compute scene flow. We now describe algorithms for each of the three cases. We also demonstrate their validity using flow results computed from multiple image sequences (captured from various viewpoints) of a non-rigid, dynamically changing scene. One such image sequence is shown in Figure 2.
Abstract— This paper presents a baseline system for automatic acoustic scene classification based on the audio signals alone. The proposed method is derived from classic, content-based, music classification approaches, and consists in a feature extraction phase followed by two dimensionality reduction steps (principal component analysis and linear discriminant analysis) and a classification phase done using a k nearest-neighbors algorithm. This paper also reports on how our system performed in the context of the DCASE 2016 challenge, for the acoustic scene classification task. Our method was ranked fifteenth amongst forty nine contest entries, and although it is below the top performing algorithms, in our perspective it is still interesting to see a low-complexity system such as ours obtain fairly good performances.
War for Talent is being experienced across the sectors as a major challenge by the HR Managers. The issues have caught the attentions among academia, industry, professional bodies and government and HR practitioners are struggling to redefine their evolving roles so that the organizational talent stays engaged and motivated in the organization. The problems of attraction and retention of the talent becomes a predominant issue across the industries and sectors. Many a time, HR Professionals are in a fix to introspect the reasons that impact employee commitment, productivity and citizenship behavior. Frequent employee attrition and High employee attrition are results of lack of attention in talent development programmes, as only 73 percent of CEOs spend a fourth of time in talent development programme (Monster.com study). The scene today needs to provide a customized approach that focuses towards aligning individual needs with the organization’s goals. In the knowledge economy, the interests of the knowledge workers cannot be ignored. Keeping these facts in view, the article attempts to address the factors responsible for talent crises and talent gaps between talent engagement and talent development.
Though we view the identification of entities as a first step toward understanding a scene from multiple descriptions, it may appear as though we’ve addressed a synthetic problem of our own invention. Our methods assume the presence of multiple descriptions for a single scene, and – as stated in Chapter 3 – we require expensive, high-quality, labeled data on which to train our supervised models. Our approach, however, extends beyond Flickr30k Entities v2, and can be used as a mechanism to automatically generate these rich annotations for similar image caption datasets. In this section, we detail the process for generating such annotations on the MSCOCO dataset (Lin et al., 2014).
For Ettinger the infant meets the maternal subject through its own primary affective compassion, the figuration for which is the co-affective encounter between not-yet I and not-yet mother in the late stage of intrauterine life. Compassion allows what she calls ‘primal psychic access to the other’ (Ettinger 2010: n.p). Like Laplanche, the encounter with the other is not so much a reaction but more like an arousal, akin to anxiety, an affective signal. Along with primary affective awe, these states mitigate early experiences of fear, guilt and shame. What this means is that alongside the primal fantasies of the primal scene, castration and seduction, that help us to understand intergenerational difference, loss and desire, Ettinger adds three new primal fantasies relating to the mother: the devouring mother, the not-enough mother and the abandoning mother. These are existential fears -- part of the condition of being human is to be anxious about being abandoned, invaded and withheld from. What is crucial, in her view, is to recognize that these are primal fantasies, distinct from narcissistic fantasies, and from actual abuses that some parents enact on their children. Primal fantasies have an important beneficial regulatory sense-giving function, and they allow the continuation of access to compassion and awe in adult life. We must be able to play with them, in order to come to terms with reality.
Most of the scene text detection algorithms in the literature can be classified into Region-based and Connected Component (CC) based approaches. Region-based methods adopted a sliding window scheme, which is basically a brute force approach which requires a lot of local decisions. Therefore, the region-based methods have focused on an efficient binary classification (text versus non text) of a small image patch. Text Localization is of fundamental importance in image understanding and content based retrieval. For instance the localization must always be achieved prior to Optical Character Recognition (OCR). Stability of such method includes robustness to noise and blurness because they accomplish features assembled throughout the region of interest. The second approach used is localizing the individual characters using the local parameters of an image (intensity, stroke-width, color, gradient etc). Feature extraction also plays a vital role in image localization process. The main goal of feature extraction is to maximize the recognition rate with minimum number of elements used in it. After analyzing existing feature descriptor methods it is found experimentally Histograms of Oriented Gradient (HOG) descriptors significantly outperform existing feature sets for character detection and best suited for the proposed system. Many researchers have made research related to this but no technique is almost perfect and they found need to improve the work in more areas at different instants and techniques.
The bidirectional reflectance of a vegetation canopy is generally assumed to be determined by the proportions of different scene components (sunlit leaves, shaded leaves, sunlit background, shaded background) presented to a sensor. The directional reflectance of individual leaves and the background may be measured, either in the field or the laboratory, but it is very difficult to characterise accurately the spatial assembledge of leaves, shadow and background that form the canopy. Conventional field spectroradiometers integrate the signal from an area of the canopy and provide no precise record of the area sensed, thus physical understanding of the interations and relationships between scene components is made more difficult, and validating canopy models at the scale of individual plants becomes almost impossible.
In this, comparing of two images related to global thresholding can detect scene change. Background and foreground pixels are the two pixels which are used calculate threshold for each pixels. Then, Compare the two foreground and background pixels and accordingly set the threshold, if foreground pixel value exceeds than the threshold set, then there is a scene change , .