text: In this paper, we propose a simple way to utilize stereo camera data to improve feature descriptors. Computer vision algorithms that use a stereo camera require some calculations of 3D information. We leverage this pre-calculated information to improve feature descriptor algorithms. We use the 3D feature information to estimate the scale of each feature. This way, each feature descriptor will be more robust to scale change without significant computations. In addition, we use stereo images to construct the descriptor vector. The SIFT and FREAK descriptors are used to evaluate the proposed method. The scale normalization technique in feature tracking test improves the standard SIFT by 8.75 and improves the standard FREAK by 28.65 . We also use a simple visual odometry algorithm and test it on the KITTI datasets. The stereo FREAK descriptor raises the number of inlier matches by 19 and consequently improves the accuracy of visual odometry by 23 .
  @cite_16: A new paradigm, Random Sample Consensus (RANSAC), for fitting a model to experimental data is introduced. RANSAC is capable of interpreting smoothing data containing a significant percentage of gross errors, and is thus ideally suited for applications in automated image analysis where interpretation is based on the data provided by error-prone feature detectors. A major portion of this paper describes the application of RANSAC to the Location Determination Problem (LDP): Given an image depicting a set of landmarks with known locations, determine that point in space from which the image was obtained. In response to a RANSAC requirement, new results are derived on the minimum number of landmarks needed to obtain a solution, and algorithms are presented for computing these minimum-landmark solutions in closed form. These results provide the basis for an automatic system that can solve the LDP under difficult viewing
  @cite_13: Most recent class-level object recognition systems work with visual words, i.e., vector quantized local descriptors. In this paper we examine the feasibility of a data- independent approach to construct such a visual vocabulary, where the feature space is discretized using a regular lattice. Using hashing techniques, only non-empty bins are stored, and fine-grained grids become possible in spite of the high dimensionality of typical feature spaces. Based on this representation, we can explore the structure of the feature space, and obtain state-of-the-art pixelwise classification results. In the case of image classification, we introduce a class-specific feature selection step, which takes the spatial structure of SIFT-like descriptors into account. Results are reported on the Graz02 dataset.
summary: One of the well-known and robust feature descriptors is . It has been successfully used for more than one decade in many applications including visual odometry, scene reconstruction, object recognition, etc. The descriptor vector contains 128 floating point numbers. The main issue with the descriptor is its computation and matching times. In real-time applications such as visual odometry or Simultaneous Localization and Mapping (), time limitation is a serious challenge. Moreover, in , it is needed to store feature descriptor vectors in order to find loop closure which is the main step of the path optimization. Therefore, the size of the descriptor vector is very important. The descriptor, which is a fast version of , constructs a 64-D descriptor vector and reduces the computation time compared to the descriptor. However, it still has high computation and matching time. @cite_16 showed that using dimensionality reduction techniques such as Principal Component Analysis (PCA) or Linear Discriminant Embedding (LDE) can reduce descriptor size without any loss in recognition performance. Another way to reduce the descriptor vector size has been presented by @cite_13 . They took advantage of a quantization method to use only 4 bits to store floating numbers of the descriptor.