What information do people use to categorize scenes? Computational scene classification models have proposed that unlocalized amplitude information, the distribution of spatial frequencies and orientations, is useful for categorizing scenes. Previous research has provided conflicting results regarding this claim. Our previous research (Loschky et al., 2007) has shown that randomly localizing am...

Attention Restoration Theory (ART) states that built scenes place greater load on attentional resources than natural scenes. This is explained in terms of "hard" and "soft" fascination of built and natural scenes. Given a lack of direct empirical evidence for this assumption we propose that perceptual saliency of scene content can function as an empirically derived indicator of fascination. Sal...

Human observers are able to rapidly and accurately categorize natural scenes, but the representation mediating this feat is still unknown. Here we propose a framework of rapid scene categorization that does not segment a scene into objects and instead uses a vocabulary of global, ecological properties that describe spatial and functional aspects of scene space (such as navigability or mean dept...

Text extraction from natural scene images is an emerging field in computer graphics. Extracted text contains important information that can be used for various purpose like vehicle number plate detection to identify the vehicle, to provide information of surrounding to visually impaired persons, preservation of information of historical documents etc. Binarization is a key process in text extra...

The ability to form a visual interpretation of the world from natural language is pivotal to human communication. Similarly, from a computational perspective, mapping descriptions of scenes to 3D geometric representations is useful in many areas such as robotics, interior design and even education. Text to 3D scene generation is a task which addresses this problem space. A user provides natural...

20 Attention is thought to impose an informational bottleneck on vision by selecting 21 particular information from visual scenes for enhanced processing. Behavioral 22 evidence suggests, however, that some scene information can be extracted even 23 when attention is directed elsewhere. Here, we investigated the neural correlates of 24 this ability by examining how attention affects electrophys...

Texture-based image retrieval is a very di cult task, especially for retrieving images of natural scene. Such images contain multiple texture patterns that may vary in intensity, scale, and orientation but still look the same to humans. Existing methods have been successful in retrieving images that contain single uniform texture but their performance deteriorates when retrieving natural scene ...

In this paper the description of 3D indoor scenes in natural language is studied from the point of view of intrinsic and relative location of the objects. An approach has been developed for this purpose which uses a XBox 360 Kinect in combination with ROS and PCL to obtain 3D-data from the scene. Object features are computed on these 3D-data, which are used to generate a SVM-model which classif...

In this paper we describe a very simple and efficient method for the détection and recognition of the Malayalam text from colour natural scene images taken by a mobile phone camera. Malayalam text detection, skew correction of the detected text ,text segmentation and character recognition are the important steps in text understanding from natural scene images. Text understanding in natural scen...

This paper presents a novel approach to natural scene segmentation. It uses both color and texture features in cooperation to provide comprehensive knowledge about every pixel in the image. A novel scheme for the collection of training samples, based on homogeneity, is proposed. Natural scene segmentation is carried out using a two-stage hierarchical self-organizing map (HSOM). The proposed met...