Over the last years, several methods have been proposed to guide the physician during reduction and fixation of bone fractures. Available solutions often use bulky instrumentation inside the operating room (OR). The latter ones usually consist of a stereo camera, placed outside the operative field, and optical markers directly attached to both the patient and the surgical instrumentation, held by the surgeon. Recently proposed techniques try to reduce the required additional instrumentation as well as the radiation exposure to both patient and physician. In this paper, we present the adaptation and the first implementation of our recently proposed video camera-based solution for screw fixation guidance. Based on the simulations conducted in our previous work, we mounted a small camera on a drill in order to recover its tip position and axis orientation w.r.t our custom-made drill sleeve with attached markers. Since drill-position accuracy is critical, we thoroughly evaluated the accuracy of our implementation. We used an optical tracking system for ground truth data collection. For this purpose, we built a custom plate reference system and attached reflective markers to both the instrument and the plate. Free drilling was then performed 19 times. The position of the drill axis was continuously recovered using both our video camera solution and the tracking system for comparison. The recorded data covered targeting, perforation of the surface bone by the drill bit and bone drilling. The orientation of the instrument axis and the position of the instrument tip were recovered with an accuracy of 1:60 +/- 1:22° and 2:03 +/- 1:36 mm respectively.
Face is one of the most popular biometric modalities. However, up to now, color is rarely actively used in face recognition. Yet, it is well-known that when a person recognizes a face, color cues can become as important as shape, especially when combined with the ability of people to identify the color of objects independent of illuminant color variations. In this paper, we examine the feasibility and effect of explicitly embedding illuminant color information in face recognition systems. We empirically examine the theoretical maximum gain of including known illuminant color to a 3D-2D face recognition system. We also investigate the impact of using computational color constancy methods for estimating the illuminant color, which is then incorporated into the face recognition framework. Our experiments show that under close-to-ideal illumination estimates, one can improve face recognition rates by 16%. When the illuminant color is algorithmically estimated, the improvement is approximately 5%. These results suggest that color constancy has a positive impact on face recognition, but the accuracy of the illuminant color estimate has a considerable effect on its benefits.
One of the most popular form of biometrics is face recognition. Face recognition techniques typically assume that a face exhibits Lambertian reectance. However, a face often exhibits prominent specularities, especially in outdoor environments. These specular highlights can compromise an identity authentication. In this work, we analyze the impact of such highlights on a 3D-2D face recognition system. First, we investigate three different specularity removal methods as preprocessing steps for face recognition. Then, we explicitly model facial specularities within the face detection system with the Cook-Torrance reflectance model. In our experiments, specularity removal increases the recognition rate on an outdoor face database by about 5% at a false alarm rate of 10-3. The integration of the Cook-Torrance model further improves these results, increasing the verification rate by 19% at a FAR of 10-3.
In orthopedic and trauma surgery, metallic plates are used for reduction and fixation of bone fractures. In clinical practice, the intra-operative planning for screw fixation is usually based on fluoroscopic images. Screw fixation is then performed on a free-hand basis. As such, multiple attempts may be required in order to achieve an optimal positioning of the fixing screws. To help the physician insert the screws in accordance to the planned position, we propose a method for screw insertion guidance. Our approach uses a small video camera, rigidly placed on the drill, and a set of small markers that are rigidly fixed on a variable angle drill sleeve. In order to investigate the achievable accuracy of our setup, we simulate the estimation of the drill bit position under two different marker arrangements, planar and 3D, and different noise levels. Furthermore, we motivate our choices for marker design and position given the limited space available for marker positioning, the requirement for accurate position estimation of the drill bit and the illumination changes that could affect the surgical site. We also describe our proposed marker detection and tracking pipeline. Our simulation results let us conclude that we can achieve an accuracy of 1° and 1mm in the estimation of angular orientation and tip position of the drill bit respectively, provided that we have accurate marker detection.
Magnetically-guided capsule endoscopy (MGCE) is a nascent technology with the goal to allow the steering of a capsule endoscope inside a water filled stomach through an external magnetic field. We developed a classification cascade for MGCE images with groups images in semantic and topological categories. Results can be used in a post-procedure review or as a starting point for algorithms classifying pathologies. The first semantic classification step discards over-/under-exposed images as well as images with a large amount of debris. The second topological classification step groups images with respect to their position in the upper gastrointestinal tract (mouth, esophagus, stomach, duodenum). In the third stage two parallel classifications steps distinguish topologically different regions inside the stomach (cardia, fundus, pylorus, antrum, peristaltic view). For image classification, global image features and local texture features were applied and their performance was evaluated. We show that the third classification step can be improved by a bubble and debris segmentation because it limits feature extraction to discriminative areas only. We also investigated the impact of segmenting intestinal folds on the identification of different semantic camera positions. The results of classifications with a support-vector-machine show the significance of color histogram features for the classification of corrupted images (97%). Features extracted from intestinal fold segmentation lead only to a minor improvement (3%) in discriminating different camera positions.
Capsule Endoscopy (CE) was introduced in 2000 and has since become an established diagnostic procedure for
the small bowel, colon and esophagus. For the CE examination the patient swallows the capsule, which then
travels through the gastrointestinal tract under the influence of the peristaltic movements. CE is not indicated
for stomach examination, as the capsule movements can not be controlled from the outside and the entire surface
of the stomach can not be reliably covered. Magnetically-guided capsule endoscopy (MGCE) was introduced in
2010. For the MGCE procedure the stomach is filled with water and the capsule is navigated from the outside
using an external magnetic field. During the examination the operator can control the motion of the capsule
in order to obtain a sufficient number of stomach-surface images with diagnostic value. The quality of the
examination depends on the skill of the operator and his ability to detect aspects of interest in real time. We
present a novel computer-assisted diagnostic-procedure (CADP) algorithm for indicating gastritis pathologies in
the stomach during the examination. Our algorithm is based on pre-processing methods and feature vectors that
are suitably chosen for the challenges of the MGCE imaging (suspended particles, bubbles, lighting). An image
is classified using an ada-boost trained classifier. For the classifier training, a number of possible features were
investigated. Statistical evaluation was conducted to identify relevant features with discriminative potential.
The proposed algorithm was tested on 12 video sequences stemming from 6 volunteers. A mean detection rate
of 91.17% was achieved during leave-one out cross-validation.
One of the biggest clues in specularity detection algorithms is the color of the specular highlights. There is a prevalent assumption that the color of specular highlights for materials like plastics and ceramics can be approximated by the color of the incident light. We will show that such an assumption is not generally appropriate because of the effects of the Fresnel reflectance coefficient and its dependence on wavelength. Our theoretical analysis shows that the sensitivity of the Fresnel term to the wavelength variations of the refractive index can be at least as large as 15%. Our experiments demonstrate that, even with traditional RGB color cameras, the recorded color of specular highlights is distinct from the color of the incident light.
Furthermore, we will show that by computing the spectral gradients (i.e. the partial derivatives of the image with respect to wavelength) at specular regions we can isolate the Fresnel term up to an additive illumination constant. Our theory is supported by experiments performed on multispectral images of different colored plastic tiles. The refractive indices of the opaque plastics were measured using a Nano-View SE MF 1000 Spectroscopic Ellipsometer. The computed spectral gradients of the tile specularities exhibited a less than 2.5% deviation from the predicted theoretical values.
An integral part of computer graphics, machine vision and
human vision understanding is modeling how a surface reflects
light. There is a substantial body of work on models describing
surface reflectance ranging from purely matte to purely specular.
One of the advantages of diffuse reflectance is that the color
and the intensity of the reflected light are separable for most
materials. Color is determined by the chromophores of the
material, while intensity depends on the scene geometry. In
specular highlights the color and the intensity of a specularity
depend on both the geometry and the index of refraction of the
material, which in turn is a function of wavelength. The graphics
and vision communities often employ the following simplifying
assumption when modeling specular highlights: For non-conductive materials the color of the specularity is the color of the light source. We will show that in most cases this assumption is violated. Theoretical analysis demonstrates that even for non-metallic surfaces the reflectivity ratio at specularities varies with both wavelength and angle of incidence. Furthermore, our experiments with a multispectral sensor clearly show that the deviation of the color of the specularities from the color of the incident light can be consistently measured.
The automated detection of humans in computer vision as well as the realistic rendering of people in computer graphics necessitates a better understanding of human skin reflectance Prior vision and graphics research on this topic has primarily focused on images acquired with conventional color cameras. Although tri-color skin data is prevalent, it does not provide adequate information for explaining skin color or for discriminating between human skin and dyes designed to mimic human skin color. A better understanding of skin reflectance can be achieved through spectrographic analysis. Previous work in this field has largely been undertaken in the medical domain and focuses on the detection of pathology. Our work concentrates on the impact of skin reflectance on the image formation process. In our radiometric facility we measure the light reflected from the skin using a high resolution, high accuracy spectrograph under precisely calibrated illumination conditions. This paper presents observations from the first body of data gathered at this facility. From the measurements collected thus far, we have observed population-independent factors of skin reflectance. We show how these factors can be exploited in skin recognition. Finally, we provide a biological explanation for the existence of a distinguishing pattern in human skin reflectance.
An invariant related to Gaussian curvature at an object point is developed based upon the covariance matrix of photometric values within a local neighborhood about the point. We employ three illumination conditions, two of which are completely unknown. We never need to explicitly know the surface normal at a point. The determinant of the covariance matrix of the intensity three-tuples in the local neighborhood of an object point is shown to be invariant with respect to rotation and translation. A way of combing these determinant to form a signature distribution is formulated that is rotation, translation, and scale invariant. This signature is shown to be invariant over large ranges of poses of the same objects, while being significantly different between distinctly shaped objects. A new object recognition methodology is proposed by compiling signatures for only a few viewpoints of a given object.
We present a novel robust methodology for corresponding a dense set of points on an object surface from photometric values, for 3-D stereo computation of depth. The methodology utilizes multiple stereo pairs of images, each stereo pair taken of exactly the same scene but under different illumination. With just 2 stereo pairs of images taken respectively for 2 different illumination conditions, a stereo pair of ratio images can be produced; one for the ratio of left images, and one for the ratio of right images. We demonstrate how the photometric ratios composing these images can be used for accurate correspondence of object points. Object points having the same photometric ratio with respect to 2 different illumination conditions comprise a well-defined equivalence class of physical constraints defined by local surface orientation relative to illumination conditions. We formally show that for diffuse reflection the photometric ratio is invariant to varying camera characteristics, surface albedo, and viewpoint and that therefore the same photometric ratio in both images of a stereo pair implies the same equivalence class of physical constraints.
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