This paper presents a large-angle imaging algorithm based on a dynamic scattering model for inverse synthetic aperture radar (ISAR). In this way, more information can be presented in an ISAR image than an ordinary RD image. The proposed model describes the scattering characteristics of ISAR target varying with different observation angles. Based on this model, feature points in each sub-image of the ISAR targets are extracted and matched using the scale-invariant feature transform (SIFT) and random sample consensus (RANSAC) algorithms. Using these feature points, high-precision rotation angles are obtained via joint estimation, which makes it possible to achieve a large angle imaging using the back-projection algorithm. Simulation results verifies the validity of the proposed method.

Local descriptors, Local Binary Pattern (LBP) and Scale Invariant Feature Transform (SIFT) are widely used in various computer applications. They emphasize different aspects of image contents. In this letter, we propose to combine them in sparse coding for categorizing scene images. First, we regularly extract LBP and SIFT features from training images. Then, corresponding to each feature, a visual word codebook is constructed. The obtained LBP and SIFT codebooks are used to create a two-dimensional table, in which each entry corresponds to an LBP visual word and a SIFT visual word. Given an input image, LBP and SIFT features extracted from the same positions of this image are encoded together based on sparse coding. After that, spatial max pooling is adopted to determine the image representation. Obtained image representations are converted into one-dimensional features and classified by utilizing SVM classifiers. Finally, we conduct extensive experiments on datasets of Scene Categories 8 and MIT 67 Indoor Scene to evaluate the proposed method. Obtained results demonstrate that combining features in the proposed manner is effective for scene categorization.

This paper presents a non-blind watermarking technique that is robust to non-linear geometric distortion attacks. This is one of the most challenging problems for copyright protection of digital content because it is difficult to estimate the distortion parameters for the embedded blocks. In our proposed scheme, the location of the blocks are recorded by the translation parameters from multiple Scale Invariant Feature Transform (SIFT) feature points. This method is based on two assumptions: SIFT features are robust to non-linear geometric distortion and even such non-linear distortion can be regarded as “linear” distortion in local regions. We conducted experiments using 149,800 images (7 standard images and 100 images downloaded from Flickr, 10 different messages, 10 different embedding block patterns, and 14 attacks). The results show that the watermark detection performance is drastically improved, while the baseline method can achieve only chance level accuracy.

An unsupervised algorithm is proposed for learning overcomplete topographic representations of nature image. Our method is based on Independent Component Analysis (ICA) model due to its superiority on feature extraction, and overcomes the weakness of traditional method in fast overcomplete learning. Besides, the learnt topographic representation, resembling receptive fields of complex cells, can be used as descriptors to extract invariant features. Recognition experiments on Caltech-101 dataset confirm that these complex cell descriptors are not only efficient in feature extraction but achieve comparable performances to traditional descriptors.

A novel Position-Invariant Robust Feature, designated as PIRF, is presented to address the problem of highly dynamic scene recognition. The PIRF is obtained by identifying existing local features (i.e. SIFT) that have a wide baseline visibility within a place (one place contains more than one sequential images). These wide-baseline visible features are then represented as a single PIRF, which is computed as an average of all descriptors associated with the PIRF. Particularly, PIRFs are robust against highly dynamical changes in scene: a single PIRF can be matched correctly against many features from many dynamical images. This paper also describes an approach to using these features for scene recognition. Recognition proceeds by matching an individual PIRF to a set of features from test images, with subsequent majority voting to identify a place with the highest matched PIRF. The PIRF system is trained and tested on 2000+ outdoor omnidirectional images and on COLD datasets. Despite its simplicity, PIRF offers a markedly better rate of recognition for dynamic outdoor scenes (ca. 90%) than the use of other features. Additionally, a robot navigation system based on PIRF (PIRF-Nav) can outperform other incremental topological mapping methods in terms of time (70% less) and memory. The number of PIRFs can be reduced further to reduce the time while retaining high accuracy, which makes it suitable for long-term recognition and localization.

In this paper, a new video frame interpolation method based on image morphing for frame rate up-conversion is proposed. In this method, image features are extracted by Scale-Invariant Feature Transform in each frame, and their correspondence in two contiguous frames is then computed separately in foreground and background regions. By using the above two functions, the proposed method accurately generates interpolation frames and thus achieves frame rate up-conversion.

A rotation invariant texture analysis technique is proposed with a novel combination of Radon Transform (RT) and Hidden Markov Models (HMM). Features of any texture are extracted during RT which due to its inherent property captures all the directional properties of a certain texture. HMMs are used for classification purpose. One HMM is trained for each texture on its feature vector which preserves the rotational invariance of feature vector in a more compact and useful form. Once all the HMMs have been trained, testing is done by picking any of these textures at any arbitrary orientation. The best percentage of correct classification (PCC) is above 98 % carried out on sixty texture of Brodatz album.

We present a robust object-based watermarking algorithm using the scale-invariant feature transform (SIFT) in conjunction with a data embedding method based on Discrete Cosine Transform (DCT). The message is embedded in the DCT domain of randomly generated blocks in the selected object region. To recognize the object region after being distorted, its SIFT features are registered in advance. In the detection scheme, we extract SIFT features from the distorted image and match them with the registered ones. Then we recover the distorted object region based on the transformation parameters obtained from the matching result using SIFT, and the watermarked message can be detected. Experimental results demonstrated that our proposed algorithm is very robust to distortions such as JPEG compression, scaling, rotation, shearing, aspect ratio change, and image filtering.

This paper presents a scale invariant face detection and classification method which uses shift invariant features extracted from a Log-Polar image. Scale changes of a face in an image are represented as shift along the horizontal axis in the Log-Polar image. In order to obtain scale invariant features, shift invariant features are extracted from each row of the Log-Polar image. Autocorrelations, Fourier spectrum, and PARCOR coefficients are used as shift invariant features. These features are then combined with simple classification methods based on Linear Discriminant Analysis to realize scale invariant face detection and classification. The effectiveness of the proposed face detection method is confirmed by experiments using face images captured under different scales, backgrounds, illuminations, and dates. To evaluate the proposed face classification method, we performed experiments using 2,800 face images with 7 scales under 2 different backgrounds and face images of 52 persons.

In this paper, we propose a new approach for describing image patterns. We integrate the concepts of multiscale image analysis, aura matrix (Gibbs random fields and cooccurrences related statistical model of texture analysis) to define image features, and to obtain the features having robustness with illumination variations and shading effects, we analyse images based on the Topographic Structure described by the Surface-Shape Operator, which describe gray-level image patterns in terms of 3D shapes instead of intensity values. Then, we illustrate usefulness of the proposed features with texture classifications. Results show that the proposed features extracted from multiscale images work much better than those from a single scale image, and confirm that the proposed features have robustness with illumination and shading variations. By comparisons with the MRSAR (Multiresolution Simultaneous Autoregressive) features using Mahalanobis distance and Euclidean distance, the proposed multiscale features give better performances for classifying the entire Brodatz textures: 112 categories, 2016 samples having various brightness in each category.