Augmenting Corner Descriptors
نویسنده
چکیده
A failing of many grey-level corner detectors is that they do not extract most of the attributes of a corner apart from its strength. This paper provides several post-processing techniques for determining additional corner attributes (i.e. colour, orientation, subtended angle, and contrast). Corner matching processes can use this additional information to resolve otherwise ambiguous correspondences and to eliminate corners whose attributes do not match certain criteria.
منابع مشابه
Object Class Detection and Classification using Multi Scale Gradient and Corner Point based Shape Descriptors
This paper presents a novel multi scale gradient and a corner point based shape descriptors. The novel multi scale gradient based shape descriptor is combined with generic Fourier descriptors to extract contour and region based shape information. Shape information based object class detection and classification technique with a random forest classifier has been optimized. Proposed integrated de...
متن کاملCorner cutting with trapezoidal augmentation for area-preserving smoothing of polygons and polylines
Recently, a new subdivision method was introduced by the author for smoothing polygons and polylines while preserving the enclosed area [Gordon D. Corner cutting and augmentation: an area-preserving method for smoothing polygons and polylines. Computer Aided Geometric Design 2010; 27(7):551–62]. The new technique, called ‘‘corner cutting and augmentation’’ (CCA), operates by cutting cornerswith...
متن کاملOnline Discriminative Feature Selection in a Bayesian Framework using Shape and Appearance
This paper presents a probabilistic Bayesian framework for object tracking using the combination of a cornerbased model and local appearance to form a locally enriched global object shape representation. A shape model is formed by corner information and it is rendered more robust and reliable by adding local descriptors to each corner. Local descriptors contribute to estimation by filtering out...
متن کاملMaximally Stable Corner Clusters: A novel distinguished region detector and descriptor
We propose a novel distinguished region detector called Maximally Stable Corner Cluster detector (MSCC). It is complementary to existing approaches like Harris-corner detectors, Difference of Gaussian detectors (DoG) or Maximally Stable Extremal Regions (MSER). The basic idea is to find distinguished regions by looking at clusters of interest points and using the concept of maximal stableness a...
متن کاملA General Purpose Feature Extractor for Light Detection and Ranging Data
Feature extraction is a central step of processing Light Detection and Ranging (LIDAR) data. Existing detectors tend to exploit characteristics of specific environments: corners and lines from indoor (rectilinear) environments, and trees from outdoor environments. While these detectors work well in their intended environments, their performance in different environments can be poor. We describe...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- CVGIP: Graphical Model and Image Processing
دوره 58 شماره
صفحات -
تاریخ انتشار 1996