Visual identification of individual animals that bear unique natural body markings is an important task in wildlife conservation. The photo databases of animal markings grow larger and each new observation has to be matched against thousands of images. Existing photo-identification solutions have constraints on image quality and appearance of the pattern of interest in the image. These constraints limit the use of photos from citizen scientists. We present a novel system for visual re-identification based on unique natural markings that is robust to occlusions, viewpoint and illumination changes. We adapt methods developed for face re-identification and implement a deep convolutional neural network (CNN) to learn embeddings for images of natural markings. The distance between the learned embedding points provides a dissimilarity measure between the corresponding input images. The network is optimized using the triplet loss function and the online semi-hard triplet mining strategy. The proposed re-identification method is generic and not species specific. We evaluate the proposed system on image databases of manta ray belly patterns and humpback whale flukes. To be of practical value and adopted by marine biologists, a re-identification system needs to have a top-10 accuracy of at least 95 . The proposed system achieves this performance standard.
Current systems used in practice (Manta Matcher @cite_5 , HotSpotter @cite_1 ) are based on automated extraction and matching of keypoint features using the Scale-Invariant Feature Transform (SIFT) algorithm @cite_37 with different modifications and enhancements to work on specific cases. While the algorithm works well on images that clearly show the pattern of interest, it is not robust to large changes in camera viewpoint, occlusions and variations in illumination.
@cite_5 @cite_37 @cite_1
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In this paper we present a prototype system for image based localization in urban environments. Given a database of views of city street scenes tagged by GPS locations, the system computes the GPS location of a novel query view. We first use a wide-baseline matching technique based on SIFT features to select the closest views in the database. Often due to a large change of viewpoint and presence of repetitive structures, a large percentage of matches (> 50 ) are not correct correspondences. The subsequent motion estimation between the query view and the reference view, is then handled by a novel and efficient robust estimation technique capable of dealing with large percentage of outliers. This stage is also accompanied by a model selection step among the fundamental matrix and the homography. Once the motion between the closest reference views is estimated, the location of the query view is then obtained by triangulation of translation directions. Approximate solutions for cases when triangulation cannot be obtained reliably are also described. The presented system is tested on the dataset used in ICCV 2005 Computer Vision Contest and is shown to have higher accuracy than previous reported results. Most of current fingerprint indexing schemes utilize features based on global textures and minutiae structures. To extend the existing technology of feature extraction, this paper proposes a new fingerprint indexing and retrieval scheme using scale invariant feature transformation (SIFT), which has been widely used in generic image retrieval. With slight loss in effectiveness, we reduce the number of features generated from one fingerprint for efficiency. To cope with the uncertainty of acquisition (e.g. partialness, distortion), we use a composite set of features to form multiple impressions for the fingerprint representation. In the index construction phase, the use of locality-sensitive hashing (LSH) allows us to perform similarity queries by only examining a small fraction of the database. Experiments on database FVC2000 and FVC2002 show the effectiveness of our proposed scheme. If a physical object has a smooth or piecewise smooth boundary, its images obtained by cameras in varying positions undergo smooth apparent deformations. These deformations are locally well approximated by affine transforms of the image plane. In consequence the solid object recognition problem has often been led back to the computation of affine invariant image local features. Such invariant features could be obtained by normalization methods, but no fully affine normalization method exists for the time being. Even scale invariance is dealt with rigorously only by the scale-invariant feature transform (SIFT) method. By simulating zooms out and normalizing translation and rotation, SIFT is invariant to four out of the six parameters of an affine transform. The method proposed in this paper, affine-SIFT (ASIFT), simulates all image views obtainable by varying the two camera axis orientation parameters, namely, the latitude and the longitude angles, left over by the SIFT method. Then it covers the other four parameters by using the SIFT method itself. The resulting method will be mathematically proved to be fully affine invariant. Against any prognosis, simulating all views depending on the two camera orientation parameters is feasible with no dramatic computational load. A two-resolution scheme further reduces the ASIFT complexity to about twice that of SIFT. A new notion, the transition tilt, measuring the amount of distortion from one view to another, is introduced. While an absolute tilt from a frontal to a slanted view exceeding 6 is rare, much higher transition tilts are common when two slanted views of an object are compared (see Figure hightransitiontiltsillustration). The attainable transition tilt is measured for each affine image comparison method. The new method permits one to reliably identify features that have undergone transition tilts of large magnitude, up to 36 and higher. This fact is substantiated by many experiments which show that ASIFT significantly outperforms the state-of-the-art methods SIFT, maximally stable extremal region (MSER), Harris-affine, and Hessian-affine. In order to solve multi-view point clouds registration in large non-feature marked scenes, a new registration and stitching method was proposed based on 2D SIFT(Scale-invariant feature transform) features. Firstly, we used texture mapping method to generate 2D effective texture image, and then extracted and match SIFT features, obtained accurate key points and registration relationship between the effective texture images. Secondly, we reflected the SIFT key points and registration relationship to the 3D point clouds data, obtained key points and registration relationship of multi-view point clouds, we can achieve multi-view point clouds stitching. Our algorithm used texture mapping method to generate 2D effective texture image, it can eliminate interference of the holes and ineffective points, and can eliminate unnecessary mistake matching. Our algorithm used correct extracted matching point pairs to stitch, avoiding stepwise iterated of ICP algorithm, so our algorithm is simple to calculate, and it's matching precision and matching efficiency are improved to some extent. We carried experiments on two-view point clouds in two large indoor; the experiment results verified the validity of our algorithm.