We identify and study two types of “accidental” images that can be formed in scenes. The first is an accidental pinhole camera image. The second class of accidental images are “inverse” pinhole camera images, formed by subtracting an image with a small occluder present from a reference image without the occluder. Both types of accidental cameras happen in a variety of different situations. For ...

Recently, scene text detection has become an active research topic in computer vision and document analysis, because of its great importance and significant challenge. However, vast majority of the existing methods detect text within local regions, typically through extracting character, word or line level candidates followed by candidate aggregation and false positive elimination, which potent...

This paper presents a binarization method for camera based natural scene (NS) images based on edge analysis and morphological dilation. Image is converted to grey scale image and edge detection is carried out using canny edge detection. The edge image is dilated using morphological dilation and analyzed to remove edges corresponding to non-text regions. The image is binarized using mean and sta...

Human scene understanding is remarkable: with only a brief glance at an image, an abundance of information is available spatial layout, scene function, semantic label, etc. Here we propose a scene-centered model of rapid human scene understanding that uses a vocabulary of global, ecological scene properties that combine to categorize natural landscape images. Behaviorally, we show human observe...

Natural urban scene images contain many problems for character recognition such as luminance noise, varying font styles or cluttered backgrounds. Detecting and recognizing text in a natural scene is a difficult problem. Several techniques have been proposed to overcome these problems. These are, however, usually based on a bottom-up scheme, which provides a lot of false positives, false negativ...

In the near future, mobile devices will be able to measure the 3D geometry of an environment using integrated depth sensing technology. This technology will enable anyone to reconstruct a 3D model of their surroundings. Similar to natural 2D images, a 3D model of a natural scene will occasionally contain a desired foreground object and an unwanted background region. Inspired by GrabCut for stil...

The Deep Boltzmann Machines (DBM) is a state-of-the-art unsupervised learning model, which has been successfully applied to handwritten digit recognition and, as well as object recognition. However, the DBM is limited in scene recognition due to the fact that natural scene images are usually very large. In this paper, an efficient scene recognition approach is proposed based on superpixels and ...