Sajid Saleem

A Robust SIFT Descriptor for Multispectral Images

This letter presents a novel method for the description of multispectral image keypoints. The method proposed is based on a modified SIFT algorithm. It uses normalized gradients as local image features for the description of keypoints in order to achieve robustness against non linear intensity changes between multispectral images. The experimental results show that the method proposed achieves a better matching performance and outperforms the SIFT algorithm.

A Modified SIFT Descriptor for Image Matching under Spectral Variations

In multispectral imaging multiple discrete wavelength bands are used to image a scene. The imaging process maps the scene contents to different intensity levels and varies the scene appearance from band to band. This induces intensity variations among the spectral images and effects the performance of SIFT for cross spectral image matching. This paper proposes modifications to the SIFT descriptor in order to improve its robustness against spectral variations. The proposed modifications are based on fact, that edges remain well preserved in multispectral imaging and we can achieve better image matching results by boosting the contribution of local edges in the SIFT descriptor construction process. Therefore, we propose a Local Contrast (Δ) and a Differential Excitation (ξ) function for the construction of SIFT descriptors. The experimental results show, that the performance of Δ-SIFT and ξ-SIFT is superior to standard SIFT for image matching under spectral variations.

A performance evaluation of SIFT and SURF for multispectral image matching

This paper evaluates the performance of SIFT and SURF for cross band matching of multispectral images. The evaluation is based on matching a reference spectral image with the images acquired at different spectral bands. The reference image possesses scale and (in-plane) rotational differences in addition to spectral variations. Additive white Gaussian noise is also added to compare performance degradation at different noise levels. We use the precision and repeatability criteria for performance evaluation. Experimental results demonstrate that SIFT performs better than SURF in multispectral environment.

Enhancing Classification Performance of Functional Near-Infrared Spectroscopy- Brain–Computer Interface Using Adaptive Estimation of General Linear Model Coefficients

In this paper, a novel methodology for enhanced classification of functional near-infrared spectroscopy (fNIRS) signals utilizable in a two-class [motor imagery (MI) and rest; mental rotation (MR) and rest] brain–computer interface (BCI) is presented. First, fNIRS signals corresponding to MI and MR are acquired from the motor and prefrontal cortex, respectively, afterward, filtered to remove physiological noises. Then, the signals are modeled using the general linear model, the coefficients of which are adaptively estimated using the least squares technique. Subsequently, multiple feature combinations of estimated coefficients were used for classification. The best classification accuracies achieved for five subjects, for MI versus rest are 79.5, 83.7, 82.6, 81.4, and 84.1% whereas those for MR versus rest are 85.5, 85.2, 87.8, 83.7, and 84.8%, respectively, using support vector machine. These results are compared with the best classification accuracies obtained using the conventional hemodynamic response. By means of the proposed methodology, the average classification accuracy obtained was significantly higher (p < 0.05). These results serve to demonstrate the feasibility of developing a high-classification-performance fNIRS-BCI.

Interest Region Description Using Local Binary Pattern of Gradients

Multispectral imaging system maps the contents of a scene to different intensity levels with in spectral images. This imaging process induces spectral variations among the different wavelength band images of the same scene and results in uncorrelated interest region descriptors for cross spectral image matching. This paper presents Local Binary Pattern of Gradients (LBPG) to improve the strength of interest region description under such spectral variations. In LBPG the image gradients are first transformed into binary patterns and then the gradient patterns are used instead of raw gradients for interest region description. We validate the LBPG approach on the spectral images of six different indoor and outdoor scenes. The experimental results confirm better cross spectral image matching performance as compared to SIFT and Center Symmetric Local Binary Patterns.

Recognition of degraded ancient characters based on dense SIFT

This paper presents a novel method for the recognition of ancient characters in historical documents. The method proposed is especially designed for degraded documents in which the character recognition based on state of the art methods is hard to achieve due to faded out ink, stain and background noise. The method proposed deals with such degradations by making use of the Dense SIFT features and the nearest neighbor distance maps. The maps encode the distances between the features of the documents and the training set. This results in local minima in the nearest neighbor distance maps which help in localization and recognition of characters in the documents. The experiments on three datasets show that the method proposed achieves a better character recognition performance compared to another method designed for similar historical documents.

Feature points for multisensor images

Abstract Feature points are effective for wide range of computer vision applications. In the last two decades, a large number of feature point detection and description algorithms have been proposed. All these algorithms are implemented differently but have a common objective to detect and describe feature points invariant to scale, rotation, intensity, and affine variations. Several comparative studies of feature points have been reported in literature. These studies are either application specific or deal with common type of transformations and deformations. Additionally, they primarily focus on evaluation of feature points on gray scale or RGB images. In contrast, this paper presents a comparison of feature points on multisensor images, which possess non linear intensity changes. The objective is to identify robust feature points for image to image matching tasks on multisensor images. Six well known feature point detector and seventeen popular descriptor algorithms are compared. Experimental results obtained on four different image datasets show that the combination of Harris detector with BRIEF descriptor outperforms all other detector-descriptor combinations on multisensor images.

Towards feature points based image matching between satellite imagery and aerial photographs of agriculture land

We present a performance comparison of state of the art feature point detector and descriptor algorithms.Comparison is carried out on aerial and satellite images of agriculture land.Objective is to identify well deserving feature points for the images of agriculture land.The agriculture land images possess high textural, photometric, and temporal differences.We also propose a new descriptor MN-SIFT, which outperforms all other descriptors on the images of agriculture land. This paper focuses on image matching between satellite imagery and aerial photographs of agriculture land. Feature points are used for image matching. The satellite imagery and aerial photographs were acquired at different times, viewpoints, sensors, and altitudes. Therefore, they possess very high temporal, photometric, and projective differences. When feature points, such as Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF) are applied on such images, they demonstrate poor performance. This paper aims at evaluating the performance of SIFT, SURF, and other state of the art feature points in order to determine well deserving feature points for the images of agriculture land. We also propose a new feature point descriptor, i.e. Modified Normalized Gradient SIFT, which obtains on average 1.73-2.37% better performance than other state of the art descriptors.

Recognizing Glagolitic Characters in Degraded Historical Documents

This paper presents a method for the recognition of Glagolitic characters in degraded historical documents. The Glagolitic character recognition is based on Dense SIFT for which image restoration is proposed as a pre-processing step in order to suppress background noise in degraded documents. Two different methods for image restoration are used which are Total Variation regularization and a new restoration method. Each method performs robustly against background noise while preserving character edges and strokes in the documents defected by stain, bleed through, and faded out ink. The experimental results achieved on three datasets show that by using image restoration as a pre-processing step to Dense SIFT generates better recognition rates for Glagolitic characters in degraded documents.

Updating Farmland Satellite Imagery Using High Resolution Aerial Images

This paper presents a new approach for updating farmland satellite imagery by registering it with high resolution aerial images. It is based on Field Boundary Junction (FBJ) as feature for registration. Fields connected to FBJ are grouped together to form FBJ descriptor. Fields are described by sampling Field Boundaries (FB) relative to FBJ locations. Every field gets different description for each FBJ connected to it. It makes FBJ descriptor unique and helps in finding the correct FBJ matches reliably. It is invariant to scaling, in-plane rotation and translation. Experimental results also show its invariance to non rigid transformations, splitting/merging of fields and localization errors in FBs and FBJs.